Targeted Proteomics Predicts a Sustained Complete-Response after Transarterial Chemoembolization and Clinical Outcomes in Patients with Hepatocellular Carcinoma: A Prospective Cohort Study

Yu, Su Jong; Kim, Hyunsoo; Min, Hophil; Sohn, Areum; Cho, Young Youn; Yoo, Jeong‐Ju; Lee, Dong Hoon; Cho, Eun Ju; Lee, Jeong-Hoon; Gim, Jungsoo; Park, Taesung; Kim, Yoon Jun; Kim, Chung Yong; Yoon, Jung‐Hwan; Kim, Youngsoo

doi:10.1021/acs.jproteome.6b00833

Cited by 26 publications

(16 citation statements)

References 39 publications

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“…It is closely related to immune function and is highly expressed in normal human lung tissues but lowly expressed in lung cancer tissues (Hummelshoj et al, 2008). LUAD and LIHC patients with low expression of FCN3 have poor prognosis, which is consistent with the findings in liver cancer and esophageal cancer (Yu et al, 2017;. Carbonic anhydrase (CA4) is zinc metalloenzyme that catalyzes the reversible hydration of carbon dioxide.…”

Section: Discussionsupporting

confidence: 64%

Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode

Xing

Zhang

et al. 2020

Front. Mol. Biosci.

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Background: The development of human tumors is associated with the abnormal expression of various functional genes, and a massive tumor-based database needs to be deeply mined. Based on a multigene prediction model, access to urgent prognosis of patients has become possible. Materials and Methods: We selected three RNA expression profiles (GSE32863, GSE10072, and GSE43458) from the lung adenocarcinoma (LUAD) database of the Gene Expression Omnibus (GEO) and analyzed the differentially expressed genes (DEGs) between tumor and normal tissue using GEO2R program. After that, we analyzed the transcriptome data of 479 LUAD samples (54 normal tissue samples and 425 cancer tissue samples) and their clinical follow-up data from the (TCGA) database. Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) were used to assess the prediction model. Multivariate Cox analysis was used to identify independent predictors. TCGA pancreatic adenocarcinoma datasets were used to establish a nomogram model. Results: We found 98 significantly prognosis-related genes using KM and COX analysis, among which six genes were found to be the DEGs in GEO. Using multivariate analysis, it was found that a single gene could not be used as an independent predictor of prognosis. However, the risk score calculated by weighting these six genes could serve as an independent prognosis predictor. COX analysis performed with multiple covariates such as age, gender, tumor stage, and TNM typing showed that risk score could still be utilized as an independent risk factor for patient survival rate (p = 0.013) and had an applicable reliability (area under the curve, AUC = 0.665). By combining risk score and various clinical features, the nomogram model was constructed, which had been proven to have high consistency for the prediction of 3-and 5-year survival rate (concordance = 0.751) and high accuracy as tested by ROC (AUC = 0.71;AUC = 0.708). Conclusion: We proposed a method to predict the prognosis of LUAD by weighting multiple genes and constructed a nomogram model suitable for the prognostic evaluation of LUAD, which could provide a new tool for the identification of therapeutic targets and the efficacy evaluation of LUAD.

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Section: Discussionsupporting

confidence: 64%

Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode

Xing

Zhang

et al. 2020

Front. Mol. Biosci.

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“…However, use of these two pathological markers would require biopsy analysis prior to TACE. Another study identified a panel of pre-TACE serum protein markers (including AFP, PIVKA-II level, LRG1, APCS, BCHE, C7, and FCN3) that can help predict TACE response of HCC (48). These factors can be investigated further to improve response prediction accuracy and HCC patient selection for TACE.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Model to Predict Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization

Morshid

Elsayes

Khalaf

et al. 2019

Radiology: Artificial Intelligence

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Purpose: Some patients with hepatocellular carcinoma (HCC) are more likely to experience disease progression despite transcatheter arterial chemoembolization (TACE) treatment, and thus would benefit from early switching to other therapeutic regimens. We sought to evaluate a fully automated machine learning algorithm that uses pre-therapeutic quantitative computed tomography (CT) image features and clinical factors to predict HCC response to TACE. Materials and Methods: Outcome information from 105 patients receiving first-line treatment with TACE was evaluated retrospectively. The primary clinical endpoint was time to progression (TTP) based on follow-up CT radiological criteria (mRECIST). A 14-week cutoff was used to classify patients as TACE-susceptible (TTP ≥14 weeks) or TACE-refractory (TTP <14 weeks). Response to TACE was predicted using a random forest classifier with the Barcelona Clinic Liver Cancer (BCLC) stage and quantitative image features as input as well as the BCLC stage alone as a control. Results: The model’s response prediction accuracy rate was 74.2% (95% CI=64%−82%) using a combination of the BCLC stage plus quantitative image features versus 62.9% (95% CI= 52%−72%) using the BCLC stage alone. Shape image features of the tumor and background liver were the dominant features correlated to the TTP as selected by the Boruta method and were used to predict the outcome. Conclusion: This preliminary study demonstrates that quantitative image features obtained prior to therapy can improve the accuracy of predicting response of HCC to TACE. This approach is likely to provide useful information for aiding HCC patient selection for TACE.

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“…44 Higher serum C7 protein level of HCC has been proposed as an indicator of poor response to transarterial chemoembolization. 45 Both PIGU and HNRNPC have been confirmed to be overexpressed in HCC and the high expression level of these 2 genes is associated with unfavorable prognosis. 46,47 In addition, MIMAT0004677 plays an oncogenic role in HCC through promoting cancer progression by directly targeting NCKAP1 and low expression of MIMAT0004677 is an indicator of favorable survival.…”

Section: Discussionmentioning

confidence: 99%

Weighted Gene Coexpression Network Analysis Reveals the Dynamic Transcriptome Regulation and Prognostic Biomarkers of Hepatocellular Carcinoma

Shi

et al. 2020

Evol Bioinform Online

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This study was aimed at revealing the dynamic regulation of mRNAs, long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) in hepatocellular carcinoma (HCC) and to identify HCC biomarkers capable of predicting prognosis. Differentially expressed mRNAs (DEmRNAs), lncRNAs, and miRNAs were acquired by comparing expression profiles of HCC with normal samples, using an expression data set from The Cancer Genome Atlas. Altered biological functions and pathways in HCC were analyzed by subjecting DEmRNAs to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Gene modules significantly associated with disease status were identified by weighted gene coexpression network analysis. An lncRNA-mRNA and an miRNA-mRNA coexpression network were constructed for genes in disease-related modules, followed by the identification of prognostic biomarkers using Kaplan-Meier survival analysis. Differential expression and association with the prognosis of 4 miRNAs were verified in independent data sets. A total of 1220 differentially expressed genes were identified between HCC and normal samples. Differentially expressed mRNAs were significantly enriched in functions and pathways related to “plasma membrane structure,” “sensory perception,” “metabolism,” and “cell proliferation.” Two disease-associated gene modules were identified. Among genes in lncRNA-mRNA and miRNA-mRNA coexpression networks, 9 DEmRNAs and 7 DEmiRNAs were identified to be potential prognostic biomarkers. MIMAT0000102, MIMAT0003882, and MIMAT0004677 were successfully validated in independent data sets. Our results may advance our understanding of molecular mechanisms underlying HCC. The biomarkers may contribute to diagnosis in future clinical practice.

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Targeted Proteomics Predicts a Sustained Complete-Response after Transarterial Chemoembolization and Clinical Outcomes in Patients with Hepatocellular Carcinoma: A Prospective Cohort Study

Cited by 26 publications

References 39 publications

Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode

Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode

A Machine Learning Model to Predict Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization

Weighted Gene Coexpression Network Analysis Reveals the Dynamic Transcriptome Regulation and Prognostic Biomarkers of Hepatocellular Carcinoma

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