The nomogram based on the 6-lncRNA model can promote the prognosis prediction of patients with breast invasive carcinoma

AbstractLong non-coding RNA (lncRNA) is a prognostic biomarker for many types of cancer. Here, we aimed to study the prognostic value of lncRNA in Breast Invasive Carcinoma (BRCA). We downloaded expression profiles from The Cancer Genome Atlas (TCGA) datasets. Subsequently, we screened the differentially expressed genes between normal tissues and tumor tissues. Univariate Cox, LASSO regression, and multivariate Cox regression analysis were used to construct a lncRNA prognostic model. Finally, a nomogram based on the lncRNAs model was developed, and weighted gene co-expression network analysis (WGCNA) was used to predict mRNAs related to the model, and to perform function and pathway enrichment. We constructed a 6-lncRNA prognostic model. Univariate and multivariate Cox regression analysis showed that the 6-lncRNA model could be used as an independent prognostic factor for BRCA patients. We developed a nomogram based on the lncRNAs model and age, and showed good performance in predicting the survival rates of BRCA patients. Also, functional pathway enrichment analysis showed that genes related to the model were enriched in cell cycle-related pathways. Tumor immune infiltration analysis showed that the types of immune cells and their expression levels in the high-risk group were significantly different from those in the low-risk group. In general, the 6-lncRNA prognostic model and nomogram could be used as a practical and reliable prognostic tool for invasive breast cancer.

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Development and validation of Pyroptosis‑related lncRNAs prediction model for bladder cancer

Bioscience Reports ◽

10.1042/bsr20212253 ◽

2022 ◽

Author(s):

Thongher Lia ◽

Yanxiang Shao ◽

Parbatraj Regmi ◽

Xiang Li

Keyword(s):

Bladder Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis

Bladder cancer is one of the highly heterogeneous disorders accompanied by a poor prognosis. This study aimed to construct a model based on pyroptosis‑related lncRNA to evaluate the potential prognostic application in bladder cancer. The mRNA expression profiles of bladder cancer patients and corresponding clinical data were downloaded from the public database from The Cancer Genome Atlas (TCGA). Pyroptosis‑related lncRNAs were identified by utilizing a co-expression network of Pyroptosis‑related genes and lncRNAs. The lncRNA was further screened by univariate Cox regression analysis. Finally, 8 pyroptosis-related lncRNA markers were established using Lasso regression and multivariate Cox regression analysis. Patients were separated into high and low-risk groups based on the performance value of the median risk score. Patients in the high-risk group had significantly poorer overall survival (OS) than those in the low-risk group (p < 0.001), and In multivariate Cox regression analysis, the risk score was an independent predictive factor of OS ( HR>1, P<0.01). The area under the curve (AUC) of the 3- and 5-year OS in the receiver operating characteristic (ROC) curve were 0.742 and 0.739 respectively. In conclusion, these 8 pyroptosis-related lncRNA and their markers may be potential molecular markers and therapeutic targets for bladder cancer patients.

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Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211041425 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110414

Author(s):

Xiaoyong Li ◽

Jiaqong Lin ◽

Yuguo pan ◽

Peng Cui ◽

Jintang Xia

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Related Gene ◽

Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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Construction and Validation of a Metabolic Reprogramming Related Prognostic Model for Hepatocellular Carcinoma

10.21203/rs.3.rs-97188/v1 ◽

2020 ◽

Author(s):

Xiaohong - Liu ◽

Qian - Xu ◽

Zi-Jing - Li ◽

Bin - Xiong

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Expression Profiles ◽

Prognostic Significance ◽

Metabolic Reprogramming ◽

Cox Regression Analysis ◽

Metabolic Genes

Abstract BackgroundMetabolic reprogramming is an important hallmark in the development of malignancies. Numerous metabolic genes have been demonstrated to participate in the progression of hepatocellular carcinoma (HCC). However, the prognostic significance of the metabolic genes in HCC remains elusive. MethodsWe downloaded the gene expression profiles and clinical information from the GEO, TCGA and ICGC databases. The differently expressed metabolic genes were identified by using Limma R package. Univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) Cox regression analysis were utilized to uncover the prognostic significance of metabolic genes. A metabolism-related prognostic model was constructed in TCGA cohort and validated in ICGC cohort. Furthermore, we constructed a nomogram to improve the accuracy of the prognostic model by using the multivariate Cox regression analysis.ResultsThe high-risk score predicted poor prognosis for HCC patients in the TCGA cohort, as confirmed in the ICGC cohort (P < 0.001). And in the multivariate Cox regression analysis, we observed that risk score could act as an independent prognostic factor for the TCGA cohort (HR (hazard ratio) 3.635, 95% CI (confidence interval)2.382-5.549) and the ICGC cohort (HR1.905, 95%CI 1.328-2.731). In addition, we constructed a nomogram for clinical use, which suggested a better prognostic model than risk score.ConclusionsOur study identified several metabolic genes with important prognostic value for HCC. These metabolic genes can influence the progression of HCC by regulating tumor biology and can also provide metabolic targets for the precise treatment of HCC.

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A six-gene-based signature for breast cancer radiotherapy sensitivity prediction

10.21203/rs.3.rs-20019/v1 ◽

2020 ◽

Author(s):

Xing Chen ◽

Junjie Zheng ◽

Min ling Zhuo ◽

Ailong Zhang ◽

Zhenhui You

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Regression Analysis ◽

Cox Regression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Breast Cancer Radiotherapy

Abstract Background: Breast cancer (BRCA) represents the most common malignancy among women worldwide that with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Methods: we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity prediction. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA tumor samples compared with their paracancerous samples in the training set were identified by using edgeR Bioconductor package followed by dimensionality reduction through autoencoder method and univariate Cox regression analysis to screen genes among DEGs that with significant prognosis significance in patients that were previously treated with radiation. LASSO Cox regression method was applied to screen optimal genes for constructing radiotherapy sensitivity prediction signature. Results: 603 DEGs were obtained in BRCA tumor samples, and seven out of which were retained after univariate cox regression analysis. LASSO Cox regression analysis finally remained six genes based on which the radiotherapy sensitivity prediction model was constructed. The signature was proved to be robust in both training and independent testing sets and an independent marker for BRCA radiotherapy sensitivity prediction. Conclusions: this study should be helpful for BRCA patients’ therapeutics selection and clinical decision.

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A novel signature based on m6A related genes as the potential prognostic factor in hepatocellular carcinoma.

10.21203/rs.2.21963/v1 ◽

2020 ◽

Author(s):

Zhuomao Mo ◽

Shaoju Luo ◽

Hao Hu ◽

Ling Yu ◽

Zhirui Cao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Roc Curve ◽

Cox Regression ◽

Risk Group ◽

Immune Therapy ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Potential Biomarker ◽

Significant Difference

Abstract Background Many different signatures and models have been established for patients with hepatocellular carcinoma (HCC), but no signature based on m6A related genes was developed. The objective of this research was to establish the signature with m6A related genes in HCC. Methods Data from 377 HCC patients from The Cancer Genome Atlas (TCGA) database was downloaded. The included m6A related genes were selected by Cox regression analysis and the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Furthermore, the nomogram was constructed and evaluated by C-index, calibration plot and ROC curve. Results The signature was established with the four m6A related genes (YTHDF2, YTHDF1, METTL3 and KIAA1429). Under the grouping from signature, patients in high risk group of showed the poor prognosis than those in low risk group. And significant difference was found in two kinds of immune cells (T cell gamma delta and NK cells activated) between two groups. The univariate and multivariate Cox regression analysis indicated that m6A related signature can be the potential independent prognosis factor in HCC. Finally, we developed a clinical risk model predicting the HCC prognosis and successfully verified it in C-index, calibration and ROC curve. Conclusion Our study identified the m6A related signature for predicting prognosis of HCC and provided the potential biomarker between m6A and immune therapy.

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Construction and Validation of Prognostic Markers of Liver Cancer Based on Autophagy Genes

Anti-Cancer Agents in Medicinal Chemistry ◽

10.2174/1871520621666210329100052 ◽

2021 ◽

Vol 21 ◽

Author(s):

Dawei Zhou ◽

Junchen Wan ◽

Jiang Luo ◽

Yuhao Tao

Keyword(s):

Regression Analysis ◽

High Risk ◽

Liver Cancer ◽

Cancer Patients ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Primary Liver Cancer ◽

Risk Groups ◽

Cox Regression Analysis

Background: Liver cancer is one of the most common diseases in the world. At present, the mechanism of autophagy genes in liver cancer is not very clear. Therefore, it is meaningful to study the role and prognostic value of autophagy genes in liver cancer. Objective: The purpose of this study is to conduct a bioinformatics analysis of autophagy genes related to primary liver cancer to establish a prognostic model of primary liver cancer based on autophagy genes. Results: Through difference analysis, 31 differential autophagy genes were screened out and then analyzed by GO and KEGG analysis. At the same time, we built a PPI network. To optimize the evaluation of the prognosis of liver cancer patients, we integrated multiple autophagy genes to establish a prognostic model. By using univariate cox regression analysis, 15 autophagy genes related to prognosis were screened out. Then we included these 15 genes into the Least Absolute Shrinkage and Selection Operator (LASSO), and performed multi-factor cox regression analysis on the 9 selected genes to construct a prognostic model. The risk score of each patient was calculated based on 4 genes(BIRC5, HSP8, SQSTM1, and TMEM74) which participated in the establishing of the model, then the patients were divided into high-risk groups and low-risk groups. In the multivariate cox regression analysis, the risk score was the independent prognostic factors (HR=1.872, 95%CI=1.544-2.196, P<0.001). Survival analysis showed that the survival time of the low-risk group was significantly longer than that of the high-risk group. Combining clinical characteristics and autophagy genes, we constructed a nomogram for predicting prognosis. The external dataset GSE14520 proved that the nomogram has a good prediction for individual patients with primary liver cancer. Conclusion: This study provided potential autophagy-related markers for liver cancer patients to predict their prognosis and revealed part of the molecular mechanism of liver cancer autophagy. At the same time, the certain gene pathways and protein pathways related to autophagy may provide some inspiration for the development of anticancer drugs.

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Identification of an autophagy-related gene expression prognostic model in endometrial carcinoma patients

10.21203/rs.3.rs-32147/v1 ◽

2020 ◽

Author(s):

Pinping Jiang ◽

Wei Sun ◽

Ningmei Shen ◽

Qiang Wang ◽

Shouyu Wang ◽

...

Keyword(s):

Clinical Features ◽

Prognostic Model ◽

Cox Regression ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Autophagy, as a lysosomal degradation pathway, has been reported to be involved in various pathologies, including cancer. However, the expression profiles of autophagy-related genes (ARGs) in endometrial cancer (EC) remain poorly understood. Methods In this study, we analyzed the expression of MRGs using The Cancer Genome Atlas (TCGA) data to screen differentially expressed MRGs (DE-MRGs) significantly correlated to EC patients’ prognosis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DE-MRGs were investigated. LASSO algorithm and Cox regression analysis were performed to select MRGs closely related to EC patients’ outcomes. A prognostic signature was developed and the efficacy were validated in part of and the entire TCGA EC cohort. Moreover, we developed a comprehensive nomogram including the risk model and clinical features to predict EC patients' survival probability. Results Ninety-four ARGs significantly dysregulated in EC samples compared with the normal control samples. Functional enrichment analysis showed these differentially expressed ARGs (DE-ARGs) were highly enriched in apoptosis, P53 signaling pathway, and various cancer development. Among the 94 DE-ARGs, we subsequently screen out four-ARGs closely related to EC patients outcomes, which are ERBB2, PTEN, TP73 and ARSA. Based on the expression and coefficiency of 4 DE-ARGs, we developed a prognostic signature and further validated its efficacy in part of and the entire TCGA EC cohort. The four ARGs signature was independent of other clinical features, and was proved to effectively distinguish high- or low-risk EC patients and predicted patients' OS accurately. Moreover, the nomogram showed the excellent consistency between the prediction and actual observation in terms of patients' 3- and 5-year survival rates. Conclusions It was suggested that the ARG prognostic model and the comprehensive nomogram may guide the precise outcome prediction and rational therapy in clinical practice.

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Construction of a Prognostic Model for Predicting Overall Survival of Patients with Colorectal Cancer

10.21203/rs.3.rs-26225/v1 ◽

2020 ◽

Author(s):

Shuwen Han ◽

Kefeng Ding

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

Regression Analysis ◽

Levulinic Acid ◽

Prognostic Model ◽

Cox Regression ◽

Aminolevulinic Acid ◽

Risk Group ◽

Prognostic Models ◽

Cox Regression Analysis

Abstract Background: Colorectal cancer (CRC) is one of the most common malignancies. The purpose of this study is to construct a prognostic model for predicting the overall survival (OS) in patients with CRC. Methods: The mRNA-seq and miRNA-seq data of colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) were downloaded from The Cancer Genome Atlas (TCGA) database. The differentially expressed RNAs (DE-RNAs) between tumor and normal tissues were screened. The Kaplan-Meier and univariate Cox regression analysis were used to screen the survival-related genes. Functional enrichment analysis of survival-related genes was conducted, followed by protein-protein interaction (PPI) analysis. Subsequently, the potential drugs targeting differentially expressed mRNAs (DE-mRNAs) were investigated. Multivariate Cox regression analysis was then conducted to screen the independent prognostic factors, and these genes were used to establish a prognostic model. A receiver operator characteristic (ROC) curve was constructed, and the area under the curve (AUC) value of ROC was calculated to evaluate the specificity and sensitivity of the model. Results: A total of 855 survival-related genes were screened. These genes were mainly enriched in Gene Ontology (GO) terms, such as methylation, synapse organization, and methyltransferase activity; and pathway analysis showed that these genes were significantly involved in N-Glycan biosynthesis and the calcium signaling pathway. PPI analysis showed that aminolevulinate dehydratase (ALAD) and cholinergic receptor muscarinic 2 (CHRM2) served vital roles in the development of CRC. Aminolevulinic acid, levulinic acid, and loxapine might be potential drugs for CRC treatment. The prognostic models were built and the patients were divided into high-risk and low-risk groups based on the median of risk score (RS) as screening threshold. The OS for patients in the high-risk group was markedly shorter than that for patients in the low-risk group. Meanwhile, kazal type serine peptidase inhibitor domain 1 (KAZALD1), hippocalcin like 4 (HPCAL4), cadherin 8 (CDH8), synaptopodin 2 (SYNPO2), cyclin D3 (CCND3), and hsa_mir_26b may be independent prognostic factors that could be considered as therapeutic targets for CRC.Conclusion: We established prognostic models that could predict the OS for CRC patients and may assist clinicians in providing personalized and precision treatment in this patient population.Highlights:1. ALAD served a vital role in the development of CRC.2. CHRM2 played a role in CRC development by affecting the calcium signaling pathway.3. Aminolevulinic acid, levulinic acid, and loxapine might be potential drugs for treating CRC.4. KAZALD1 and HPCAL4 were associated with the OS of CRC.5. CDH8, SYNPO2, CCND3, and hsa-mir-26b were closely related to the prognostic of CRC staging.

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Biological Functions and Prognostic Value of Ferroptosis-Related Genes in Bladder Cancer

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.631152 ◽

2021 ◽

Vol 8 ◽

Author(s):

Kezhen Yi ◽

JingChong Liu ◽

Yuan Rong ◽

Cheng Wang ◽

Xuan Tang ◽

...

Keyword(s):

Bladder Cancer ◽

Protein Interactions ◽

Prognostic Value ◽

Prognostic Model ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Prognostic Information ◽

Cox Regression Analysis

Background: Every year, nearly 170,000 people die from bladder cancer worldwide. A major problem after transurethral resection of bladder tumor is that 40–80% of the tumors recur. Ferroptosis is a type of regulatory necrosis mediated by iron-catalyzed, excessive oxidation of polyunsaturated fatty acids. Increasing the sensitivity of tumor cells to ferroptosis is a potential treatment option for cancer. Establishing a diagnostic and prognostic model based on ferroptosis-related genes may provide guidance for the precise treatment of bladder cancer.Methods: We downloaded mRNA data in Bladder Cancer from The Cancer Genome Atlas and analyzed differentially expressed genes based on and extract ferroptosis-related genes. We identified relevant pathways and annotate the functions of ferroptosis-related DEGs using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and Gene Ontology functions. On the website of Search Tool for Retrieving Interacting Genes database (STRING), we downloaded the protein-protein interactions of DEGs, which were drawn by the Cytoscape software. Then the Cox regression analysis were performed so that the prognostic value of ferroptosis-related genes and survival time are combined to identify survival- and ferroptosis-related genes and establish a prognostic formula. Survival analysis and receiver operating characteristic curvevalidation were then performed. Risk curves and nomograms were generated for both groups to predict survival. Finally, RT-qPCR was applied to analyze gene expression.Results: Eight ferroptosis-related genes with prognostic value (ISCU, NFE2L2, MAFG, ZEB1, VDAC2, TXNIP, SCD, and JDP2) were identified. With clinical data, we established a prognostic model to provide promising diagnostic and prognostic information of bladder cancer based on the eight ferroptosis-related genes. RT-qPCR revealed the genes that were differentially expressed between normal and cancer tissues.Conclusion: This study found that the ferroptosis-related genes is associated with bladder cancer, which may serve as new target for the treatment of bladder cancer.

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Identification of Biomarkers of Hepatocellular Carcinoma Gene Prognosis Based on Immune-Related lncRNA Signature of Transcriptome Data

10.21203/rs.3.rs-57303/v1 ◽

2020 ◽

Author(s):

Andi Ma ◽

Yukai Sun ◽

Racheal O. Ogbodu ◽

Ling Xiao ◽

Haibing Deng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis ◽

Cox Regression Analysis

Abstract Background: It is well known that long non-coding RNAs (lncRNAs) play a vital role in cancer. We aimed to explore the prognostic value of potential immune-related lncRNAs in hepatocellular carcinoma (HCC). Methods: Validated the established lncRNA signature of 343 patients with HCC from The Cancer Genome Atlas (TCGA) and 81 samples from Gene Expression Omnibus (GEO). Immune-related lncRNAs for HCC prognosis were evaluated using Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. LASSO analysis was performed to calculate a risk score formula to explore the difference in overall survival between high- and low-risk groups in TCGA, which was verified using GEO, Gene Ontology (GO), and pathway-enrichment analysis. These analyses were used to identify the function of screened genes and construct a co-expression network of these genes. Results: Using computational difference algorithms and lasso Cox regression analysis, the differentially expressed and survival-related immune-related genes (IRGs) among patients with HCC were established as five novel immune-related lncRNA signatures (AC099850.3, AL031985.3, PRRT3-AS1, AC023157.3, MSC-AS1). Patients in the low‐risk group showed significantly better survival than patients in the high‐risk group ( P = 3.033e−05). The signature identified can be an effective prognostic factor to predict patient survival. The nomogram showed some clinical net benefits predicted by overall survival. In order to explore its underlying mechanism, several methods of enrichment were elucidated using Gene Set Enrichment Analysis. Conclusion: Identifying five immune-related lncRNA signatures has important clinical implications for predicting patient outcome and guiding tailored therapy for patients with HCC with further prospective validation.

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