Identification of a protein signature for predicting overall survival of hepatocellular carcinoma: a study based on data mining

Wu, Zeng-Hong; Yang, Dongliang

doi:10.1186/s12885-020-07229-x

Cited by 12 publications

(12 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, ssGSEA revealed significant differences in aDCs, mast cells, MHC class I and type II IFN responses between the two risk cohorts. Several prior analyses have also shown that certain gene signatures may predict patient survival (20)(21)(22)(23)(24)(25)(26); however, our study has some differences and/or advantages compared with similar analyses. First, the gene signatures built in previous studies require many genes (20)(21)(22)(23), which possibly leads to some difficulties in real-world practice.…”

Section: Discussionmentioning

confidence: 80%

Identification of a Novel Four-Gene Signature Correlated With the Prognosis of Patients With Hepatocellular Carcinoma: A Comprehensive Analysis

Zhu

2021

Front. Oncol.

View full text Add to dashboard Cite

PurposeHepatocellular carcinoma (HCC) is a common solid-tumor malignancy with high heterogeneity, and accurate prognostic prediction in HCC remains difficult. This analysis was performed to find a novel prognostic multigene signature.MethodsThe TCGA-LIHC dataset was analyzed for differentially coexpressed genes through weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. A protein-protein interaction (PPI) network and univariate Cox regression analysis of overall survival (OS) were utilized to identify their prognostic value. Next, we used least absolute shrinkage and selection operator (LASSO) Cox regression to establish a prognostic module. Subsequently, the ICGC-LIRI-JP dataset was applied for further validation. Based on this module, HCC cases were stratified into high-risk and low-risk groups, and differentially expressed genes (DEGs) were identified. Functional enrichment analyses of these DEGs were conducted. Finally, single-sample gene set enrichment analysis (ssGSEA) was performed to explore the correlation between the prognostic signature and immune status.ResultsA total of 393 differentially coexpressed genes were obtained. Forty differentially coexpressed hub genes were identified using the CytoHubba plugin, and 38 of them were closely correlated with OS. Afterward, we established the four-gene prognostic signature with an acceptable accuracy (area under the curve [AUC] of 1-year survival: 0.739). The ICGC-LIRI-JP dataset also supported the acceptable accuracy (AUC of 1-year survival:0.752). Compared with low-risk cohort, HCC cases in the high-risk cohort had shorter OS, higher tumor grades, and higher T stages. The risk scores of this signature still act as independent predictors of OS (P<0.001). Functional enrichment analyses suggest that it was mainly organelle fission and nuclear division that were enriched. Finally, ssGSEA revealed that this signature is strongly associated with the immune status of HCC patients.ConclusionsThe proposed prognostic signature of four differentially coexpressed hub genes has satisfactory prognostic ability, providing important insight into the prediction of HCC prognosis.

show abstract

Section: Discussionmentioning

confidence: 80%

Identification of a Novel Four-Gene Signature Correlated With the Prognosis of Patients With Hepatocellular Carcinoma: A Comprehensive Analysis

Zhu

2021

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…In comparison, tumor protein biomarkers are more practical and reliable ( Russell et al, 2019 ; Sun N. et al, 2020 ). In addition, protein signature has been studied in bladder urothelial carcinoma ( Luo and Zhang, 2020 ), colorectal cancer ( Yue et al, 2020 ), hepatocellular carcinoma ( Wu and Yang, 2020 ), head and neck squamous cell carcinoma ( Wu et al, 2020 ), lung squamous cell carcinoma ( Fang et al, 2020 ), esophageal squamous cell carcinoma ( Wang et al, 2018 ), and pancreatic ductal adenocarcinoma ( Burki, 2017 ), but in patients with ccRCC, related studies are rare. Therefore, our research will be enlightening and valuable to other researchers.…”

Section: Discussionmentioning

confidence: 99%

“…Based on this, studies focused on proteomics have sprung up as a result ( Menschaert and Fenyö, 2017 ). The proteomic studies set their focus on the study of proteins at a large scale ( Wu and Yang, 2020 ; Wu et al, 2020 ). They could be widely combined with genomics, epigenomics, transcriptomics, and other novels multi-omics analysis to reveal novel therapeutic targets or potential biomarkers, which could drive new strategies for diagnosis and treatment and many proteins have been widely used in clinical therapy as important drug targets ( Menschaert et al, 2010 ), such as PD-1, PD-L1 and CTLA-4 ( Aggen et al, 2020 ; Gulati and Vaishampayan, 2020 ; Xu et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of a Novel Protein-Based Signature to Improve Prognosis Prediction in Renal Clear Cell Carcinoma

Chu

Zhu

et al. 2021

Front. Mol. Biosci.

View full text Add to dashboard Cite

BackgroundClear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney cancer, and its incidence and mortality are not optimistic. It is well known that tumor-related protein markers play an important role in cancer detection, prognosis prediction, or treatment selection, such as carcinoembryonic antigen (CEA), programmed cell death 1 (PD-1), programmed cell death 1 ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4), so a comprehensive analysis was performed in this study to explore the prognostic value of protein expression in patients with ccRCC.Materials and MethodsProtein expression data were obtained from The Cancer Proteome Atlas (TCPA), and clinical information were downloaded from The Cancer Genome Atlas (TCGA). We selected 445 patients with complete information and then separated them into a training set and testing set. We performed univariate, least absolute shrinkage and selection operator (LASSO) Cox analyses to find prognosis-related proteins (PRPs) and constructed a protein signature. Then, we used stratified analysis to fully verify the prognostic significance of the prognostic-related protein signature score (PRPscore). Besides, we also explored the differences in immunotherapy response and immune cell infiltration level in high and low score groups. The consensus clustering analysis was also performed to identify potential cancer subgroups.ResultsFrom the training set, a total of 233 PRPs were selected, and a seven-protein signature was constructed, including ACC1, AR, MAPK, PDK1, PEA15, SYK, and BRAF. Based on the PRPscore, patients could be divided into two groups with significantly different overall survival rates. Univariate and multivariate Cox regression analyses proved that this signature was an independent prognostic factor for patients (P < 0.001). Moreover, the signature showed a high ability to distinguish prognostic outcomes among subgroups, and the low score group had a better prognosis (P < 0.001) and better immunotherapy response (P = 0.003) than the high score group.ConclusionWe constructed a novel protein signature with robust predictive power and high clinical value. This will help to guide the disease management and individualized treatment of ccRCC patients.

show abstract

“…Analysis of proteomic data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) and validated in The Cancer Proteome Atlas (TCPA) for 159 patients diagnosed with HBV-related HCC revealed 422 DEPs [ 184 ]. Among them were survival-associated proteins including proliferating cell nuclear antigen (PCNA), MutS homolog 6 (MSH6), cyclin-dependent kinase 1 (CDK1), and asparagine synthetase (ASNS) ( Figure 5 ).…”

Section: Screening For Novel Hcc Proteomic Biomarker Candidatesmentioning

confidence: 99%

Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine

et al. 2021

View full text Add to dashboard Cite

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver with high morbidity and mortality rates worldwide. Since 1963, when alpha-fetoprotein (AFP) was discovered as a first HCC serum biomarker, several other protein biomarkers have been identified and introduced into clinical practice. However, insufficient specificity and sensitivity of these biomarkers dictate the necessity of novel biomarker discovery. Remarkable advancements in integrated multiomics technologies for the identification of gene expression and protein or metabolite distribution patterns can facilitate rising to this challenge. Current multiomics technologies lead to the accumulation of a huge amount of data, which requires clustering and finding correlations between various datasets and developing predictive models for data filtering, pre-processing, and reducing dimensionality. Artificial intelligence (AI) technologies have an enormous potential to overcome accelerated data growth, complexity, and heterogeneity within and across data sources. Our review focuses on the recent progress in integrative proteomic profiling strategies and their usage in combination with machine learning and deep learning technologies for the discovery of novel biomarker candidates for HCC early diagnosis and prognosis. We discuss conventional and promising proteomic biomarkers of HCC such as AFP, lens culinaris agglutinin (LCA)-reactive L3 glycoform of AFP (AFP-L3), des-gamma-carboxyprothrombin (DCP), osteopontin (OPN), glypican-3 (GPC3), dickkopf-1 (DKK1), midkine (MDK), and squamous cell carcinoma antigen (SCCA) and highlight their functional significance including the involvement in cell signaling such as Wnt/β-catenin, PI3K/Akt, integrin αvβ3/NF-κB/HIF-1α, JAK/STAT3 and MAPK/ERK-mediated pathways dysregulated in HCC. We show that currently available computational platforms for big data analysis and AI technologies can both enhance proteomic profiling and improve imaging techniques to enhance the translational application of proteomics data into precision medicine.

show abstract

Identification of a protein signature for predicting overall survival of hepatocellular carcinoma: a study based on data mining

Cited by 12 publications

References 32 publications

Identification of a Novel Four-Gene Signature Correlated With the Prognosis of Patients With Hepatocellular Carcinoma: A Comprehensive Analysis

Identification of a Novel Four-Gene Signature Correlated With the Prognosis of Patients With Hepatocellular Carcinoma: A Comprehensive Analysis

Identification of a Novel Protein-Based Signature to Improve Prognosis Prediction in Renal Clear Cell Carcinoma

Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine

Contact Info

Product

Resources

About