Screening of significant biomarkers with poor prognosis in hepatocellular carcinoma via bioinformatics analysis

Sun, Qianli; Liu, Peng; Lü, Bin; Zhu, Yuan; Liu, Tongxin

doi:10.1097/md.0000000000021702

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…It has been confirmed that RRM2 is a potential biomarker in a variety of tumors ( 12 - 15 ). In HCC, studies have found that overexpression of RRM2 is linked to low rates of survival ( 16 ), and expression of RRM2 is strongly positively linked to infiltration of immune cells and immune checkpoint expression, the upregulation of RRM2 mediated by ncRNAs correlates with poor prognosis and tumor immune infiltration of HCC ( 17 ). Through Kaplan-Meier analysis, this research showed an association between worsening prognosis of individuals with HCC with elevated expression levels of RRM2 (P<0.01) by examining 286 TCGA samples.…”

Section: Discussionmentioning

confidence: 99%

Computed tomography radiomics signature via machine learning predicts RRM2 and overall survival in hepatocellular carcinoma

Li¹,

Long²,

Lin³

et al. 2023

J Gastrointest Oncol

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Background Radiomics can be used to noninvasively predict molecular markers to address the clinical dilemma that some patients cannot accept invasive procedures. This research evaluated the prognostic significance of the expression level of ribonucleotide reductase regulatory subunit M2 ( RRM2 ) in individuals with hepatocellular carcinoma (HCC) and established a radiomics model for predicting the RRM2 expression level. Methods Genomic data for HCC patients and corresponding computed tomography (CT) images were accessed at The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which were utilized for prognosis analysis, radiomic feature extraction and model construction, respectively. The maximum relevance minimum redundancy algorithm (mRMR) and recursive feature elimination (RFE) were used for feature selection. Following feature extraction, a logistic regression algorithm was fitted to establish a dichotomous model that predicts RRM2 gene expression. Establishment of the radiomics nomogram was carried out using the Cox regression model. Receiver operating characteristic (ROC) curve analysis was employed to assess the model performance. Clinical utility was determined by decision curve analysis (DCA). Results High RRM2 expression acted as a risk factor for overall survival (OS) [hazard ratio (HR) =2.083, P<0.001] and was implicated in regulation of the immune response. Four optimal radiomics features were selected for prediction of RRM2 expression. A predictive nomogram was established using the clinical variables and radiomics score (RS), and the areas under the ROC curve (AUCs) of the time-dependent ROC curve of the model were 0.836, 0.757, and 0.729 for the 1-, 3-, and 5-year periods, respectively. DCA confirmed that the nomogram had good clinical usefulness. Conclusions The RRM2 expression level in HCC can considerably affect prognosis of these patients. Expression levels of RRM2 and prognosis of HCC individuals can be predicted through radiomics features by utilizing CT scan data.

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Section: Discussionmentioning

confidence: 99%

Computed tomography radiomics signature via machine learning predicts RRM2 and overall survival in hepatocellular carcinoma

Li¹,

Long²,

Lin³

et al. 2023

J Gastrointest Oncol

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“…However, the prognosis of patients with colon cancer varies by multiple factors, including the clinical histological subtypes, age, genetic profiles, and treatment responses [4][5][6][7][8]. Also, the unsatisfactory prognostic outcomes still exist due to the complex pathogenesis that involves a variety of molecular or genetic factors [3,[9][10][11][12]. Therefore, the identification of prognostic biomarkers for colon cancer is still necessary.…”

Section: Introductionmentioning

confidence: 99%

Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis

Fang

Xie

et al. 2021

World J Surg Onc

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Background Colon cancer is a worldwide leading cause of cancer-related mortality, and the prognosis of colon cancer is still needed to be improved. This study aimed to construct a prognostic model for predicting the prognosis of colon cancer. Methods The gene expression profile data of colon cancer were obtained from the TCGA, GSE44861, and GSE44076 datasets. The WGCNA module genes and common differentially expressed genes (DEGs) were used to screen out the prognosis-associated DEGs, which were used to construct a prognostic model. The performance of the prognostic model was assessed and validated in the TCGA training and microarray validation sets (GSE38832 and GSE17538). At last, the model and prognosis-associated clinical factors were used for the construction of the nomogram. Results Five colon cancer-related WGCNA modules (including 1160 genes) and 1153 DEGs between tumor and normal tissues were identified, inclusive of 556 overlapping DEGs. Stepwise Cox regression analyses identified there were 14 prognosis-associated DEGs, of which 12 DEGs were included in the optimized prognostic gene signature. This prognostic model presented a high forecast ability for the prognosis of colon cancer both in the TCGA training dataset and the validation datasets (GSE38832 and GSE17538; AUC > 0.8). In addition, patients’ age, T classification, recurrence status, and prognostic risk score were associated with the prognosis of TCGA patients with colon cancer. The nomogram was constructed using the above factors, and the predictive 3- and 5-year survival probabilities had high compliance with the actual survival proportions. Conclusions The 12-gene signature prognostic model had a high predictive ability for the prognosis of colon cancer.

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Computational approaches to identify biomarkers, enzymes, and pathways of hepatocellular carcinoma

Chaitanya

Priya

Malla

2022

Theranostics and Precision Medicine for the Management of Hepatocellular Carcinoma, Volume 2

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Screening of significant biomarkers with poor prognosis in hepatocellular carcinoma via bioinformatics analysis

Cited by 4 publications

References 42 publications

Computed tomography radiomics signature via machine learning predicts RRM2 and overall survival in hepatocellular carcinoma

Computed tomography radiomics signature via machine learning predicts RRM2 and overall survival in hepatocellular carcinoma

Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis

Computational approaches to identify biomarkers, enzymes, and pathways of hepatocellular carcinoma

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