An ensemble prognostic model for colorectal cancer.
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ABSTRACT:
SUBMITTER: Li BQ
PROVIDER: S-EPMC3642113 | biostudies-literature | 2013
REPOSITORIES: biostudies-literature
Publications
An ensemble prognostic model for colorectal cancer.
Li Bi-Qing BQ Huang Tao T Zhang Jian J Zhang Ning N Huang Guo-Hua GH Liu Lei L Cai Yu-Dong YD
PloS one 20130502 5
Colorectal cancer can be grouped into Dukes A, B, C, and D stages based on its developments. Generally speaking, more advanced patients have poorer prognosis. To integrate progression stage prediction systems with recurrence prediction systems, we proposed an ensemble prognostic model for colorectal cancer. In this model, each patient was assigned a most possible stage and a most possible recurrence status. If a patient was predicted to be recurrence patient in advanced stage, he would be classi ...[more]
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| S-EPMC7729086 | biostudies-literature
Project description:BackgroundColorectal cancer (CRC) is a kind of gastrointestinal tumor with serious high morbidity and mortality. Several reports have implicated the disorder of RNA-binding proteins (RBPs) in plenty of tumors, associating it to tumorigenesis and disease progression. The study is intended to construct novel prognostic biomarkers associated with CRC patients.MethodsData of gene expression was acquired from the TCGA database, prognosis-related genes were selected. Besides, we analyzed GO and KEGG pathways. Univariate and multivariate Cox analyses were performed to generate a prognostic-related gene signature, which was evaluated by the Kaplan-Meier (K-M) and the Receiver Operating Characteristic (ROC) curve. The independent prognostic factor was established by survival analysis. GSE38832 dataset was used to validate the signature. Finally, expression of 8 genes was further confirmed by qRT-PCR in SW480 and SW620 cell lines.ResultsWe obtained 224 differentially expressed RBPS in total, of which 78 were downregulated and 146 were upregulated. Univariate COX analysis was conducted in the TCGA cohort to select 13 RBPs with P < 0.005, stepwise multivariate COX regression analysis was used to construct an 8-RBP signature (TERT, PPARGC1A, BRCA1, CELF4, TDRD7, LUZP4, PNLDC1, ZC3H12C). Based on the model, systematic analysis illustrated that a high risk score was obviously connected to a poor prognosis. The prognostic value of the risk score was validated in GSE38832 dataset, indicating that the risk model was accurate and effective. The prognostic signature-based risk score was identified as an independent prognostic indicator for CRC. The expression results of qRT-PCR were consistent with the results of differential expression analysis.ConclusionsThe eight-RBP signature can predict the survival of CRC patients and potentially act as CRC prognostic biomarker.
| S-EPMC8182183 | biostudies-literature
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| S-EPMC7780303 | biostudies-literature
Project description:An increasing number of studies have shown that abnormal metabolism processes are closely correlated with the genesis and progression of colorectal cancer (CRC). In this study, we systematically explored the prognostic value of metabolism-related genes (MRGs) for CRC patients. A total of 289 differentially expressed MRGs were screened based on The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB), and 72 differentially expressed transcription factors (TFs) were obtained from TCGA and the Cistrome Project database. The clinical samples obtained from TCGA were randomly divided at a ratio of 7?:?3 to obtain the training group (n = 306) and the test group (n = 128). After univariate and multivariate Cox regression analyses, we constructed a prognostic model based on 6 MRGs (AOC2, ENPP2, ADA, GPD1L, ACADL, and CPT2). Kaplan-Meier survival analysis of the training group, validation group, and overall samples proved that the model had statistical significance in predicting the outcomes of patients. Independent prognosis analysis suggested that this risk score might serve as an independent prognosis factor for CRC patients. Moreover, we combined the prognostic model and the clinical characteristics in a nomogram to predict the overall survival of CRC patients. Furthermore, gene set enrichment analysis (GSEA) was conducted to identify the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in the high- and low-risk groups, which might provide novel therapeutic targets for CRC patients. We discovered through the protein-protein interaction (PPI) network and TF-MRG regulatory network that 7 hub genes were retrieved from the PPI network and 4 kinds of differentially expressed TFs (NR3C1, MYH11, MAF, and CBX7) positively regulated 4 prognosis-associated MRGs (GSTM5, PTGIS, ENPP2, and P4HA3).
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Project description:BackgroundColorectal cancer (CRC) is one of the most common malignant tumors with a poor prognosis. At present, the pathogenesis is not completely clear. Therefore, finding reliable prognostic indicators for CRC is of important clinical significance. In this study, bioinformatics methods were used to screen the prognostic immune-related lncRNAs of CRC, and a prognostic risk scoring model based on immune-related lncRNAs signatures were constructed to provide a basis for prognostic evaluation and immunotherapy of CRC patients.MethodsThe clinical information and RNA-seq data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database. The information of immune-related lncRNA was downloaded from the immunology database and analysis portal. The differentially expressed immune-related lncRNAs (IRLs) were screened by the edgeR package of R software. The prognostic value of IRLs was studied. Based on Cox regression analysis, a prognostic index (IRLPI) based on IRLs was established, and the relationship between the risk score and the clinicopathological characteristics of CRC was analyzed to determine the effectiveness of the risk score model as an independent prognostic factor.ResultsA total of 240 differentially expressed IRLs were identified between normal colorectal cancer tissues and normal colorectal cancer tissues, in which 8 were significantly associated with the survival of CRC patients (P < 0.05), including LINC00461, LINC01055, ELFN1-AS1, LMO7-AS1, CYP4A22-AS1, AC079612.1, LINC01351, and MIR31HG. And most of the lncRNAs related to survival were risk factors for the prognosis of CRC. The index established based on the 7 survival-related IRLs found to be highly accurate in monitoring CRC prognosis. Besides, IRLPI was significantly correlated with a variety of pathological factors and immune cell infiltration.ConclusionEight immune-related lncRNAs closely related to the prognosis of CRC patients were identified from the TCGA database. At the same time, an independent IRLPI was constructed, which may be helpful for clinicians to assess the prognosis of patients with CRC and to formulate individualized treatment plans.
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Project description:BackgroundTo construct a model that could effectively predict the prognosis of colorectal cancer (CRC) by searching for methylated-differentially expressed genes (MDEGs).MethodsWe identified MDEGs through four databases from Gene Expression Omnibus (GEO) and annotated their functions via bioinformatics analysis. Subsequently, after adjusting for gender, age, and grading, multivariate Cox hazard analysis was utilized to select MDEGs interrelated with the prognosis of CRC, and LASSO analysis was utilized to fit the prediction model in the training set. Furthermore, another independent dataset was harnessed to verify the effectiveness of the model in predicting prognosis.ResultsIn total, 252 hypomethylated and up-regulated genes and 132 hypermethylated and down-regulated genes were identified, 27 of which were correlated with the prognosis of CRC, and a 10-gene prognostic model was established after LASSO analysis. The overall survival rate could be effectively grouped into different risks by the median score of this model in the training set [risk ratio (HR) =2.27, confidence interval (95% CI), 1.69-3.13, P=8.15×10-8], and the validity of its effect in predicting prognosis in CRC was verified in the validation dataset (HR =1.75, 95% CI, 1.15-2.70, P=9.32×10-3).ConclusionsOur model could effectively predict the overall survival rate of patients with CRC and provides potential application guidelines for its clinically personalized treatment.
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Project description:Colorectal cancer (CRC) is the third most commonly diagnosed cancer in both men and women in the United States, with an estimated 153,760 new cases predicted for 2007. Since the 1960s, 5-fluorouracil (5-FU) has remained the mainstay of therapeutic options in the treatment of advanced CRC, with response rates of 20% to 25%. The introduction of newer agents such as oxaliplatin and irinotecan in combination with 5-FU has increased response rates to 40% to 50% in advanced disease and improved overall survival. The development of monoclonal antibodies targeting the epidermal growth factor receptor or vascular endothelial growth factor has demonstrated additional clinical benefit for patients with metastatic disease. However, many patients succumb to their disease, and a significant proportion will experience severe chemotherapy-associated toxicities while deriving little or no benefit. To improve the treatment of CRC, efforts must be directed toward the identification of patients who are likely to respond to a specific therapy, those who will experience severe toxicities, and those who will benefit from chemotherapy in the adjuvant setting. However, the utility of individual markers of response, toxicity, and disease recurrence remains in question. Efforts are now under way to develop multimarker profiles that can more accurately predict disease response. In this review, we discuss both predictive and prognostic markers identified in the treatment of CRC in terms of their robustness and their ability to assist the clinician in developing the most efficacious and least toxic therapeutic strategy for each patient.
| S-EPMC2631215 | biostudies-other
Project description:Colorectal cancer (CRC) is one of the most prevalent and fatal malignancies, and novel biomarkers for the diagnosis and prognosis of CRC must be identified. RNA-binding proteins (RBPs) are essential modulators of transcription and translation. They are frequently dysregulated in various cancers and are related to tumorigenesis and development. The mechanisms by which RBPs regulate CRC progression are poorly understood and no clinical prognostic model using RBPs has been reported in CRC. We sought to identify the hub prognosis-related RBPs and to construct a prognostic model for clinical use. mRNA sequencing and clinical data for CRC were obtained from The Cancer Genome Atlas database (TCGA). Gene expression profiles were analyzed to identify differentially expressed RBPs using R and Perl software. Hub RBPs were filtered out using univariate Cox and multivariate Cox regression analysis. We used functional enrichment analysis, including Gene Ontology and Gene Set Enrichment Analysis, to perform the function and mechanisms of the identified RBPs. The nomogram predicted overall survival (OS). Calibration curves were used to evaluate the consistency between the predicted and actual survival rate, the consistency index (c-index) was calculated, and the prognostic effect of the model was evaluated. Finally, we identified 178 differently expressed RBPs, including 121 up-regulated and 57 down-regulated proteins. Our prognostic model was based on nine RBPs (PNLDC1, RRS1, HEXIM1, PPARGC1A, PPARGC1B, BRCA1, CELF4, AEN and NOVA1). Survival analysis showed that patients in the high-risk subgroup had a worse OS than those in the low-risk subgroup. The area under the curve value of the receiver operating characteristic curve of the prognostic model is 0.712 in the TCGA cohort and 0.638 in the GEO cohort. These results show that the model has a moderate diagnostic ability. The c-index of the nomogram is 0.77 in the TCGA cohort and 0.73 in the GEO cohort. We showed that the risk score is an independent prognostic biomarker and that some RBPs may be potential biomarkers for the diagnosis and prognosis of CRC.
| S-EPMC8029696 | biostudies-literature
Project description:Background Patients with metastatic colorectal cancer (mCRC) who progress on standard therapies may be eligible for phase I trials. To better delineate the risk-benefit ratio, we assessed toxicities, clinical outcomes and prognostic factors. Methods Records of mCRC patients on phase I trials at our institution over 18 years were reviewed. Univariable (UVA) and multivariable analyses (MVA) were undertaken and a prognostic model developed. Results There were 187 enrollments on 37 phase I trials. Median age was: 59 (29-83) years and number of prior therapies: 3 (0-8). The clinical benefit rate (CBR): response (5.6%) + stable disease, was 43.1%. Median progression free survival (PFS) and overall survival (OS) was 7.7 weeks and 43.7 weeks, respectively. The MVA identified age > 60 years (HR 1.63, p < 0.004), albumin<3.5 g/dL (HR 3.69, p < 0.001), direct bilirubin>ULN (HR1.69, p < 0.01), and WBC ≥ 5.2 k/uL (HR 1.97, p < 0.001) as negative prognostic factors. A risk score based on the MVA revealed that patients with a score of 0-1 had an improved OS (58.7 weeks) compared to a score of 2 (49.9 weeks, p < 0.01) and 3 (14.1 weeks, p < 0.001). Conclusions Phase 1 trials may offer similar or better clinical outcome for mCRC patients than standard third line therapies; the prognostic model could assist in selecting appropriate patients.
| S-EPMC6461536 | biostudies-literature