Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma Using Large-Scale Transcriptomics Data

Abstract

The high mortality rate of hepatocellular carcinoma (HCC) is primarily due to its late diagnosis. In the past, numerous attempts have been made to design genetic biomarkers for the identification of HCC; unfortunately, most of the studies are based on small datasets obtained from a specific platform or lack reasonable validation performance on the external datasets. In order to identify a universal expression-based diagnostic biomarker panel for HCC that can be applicable across multiple platforms, we have employed large-scale transcriptomic profiling datasets containing a total of 2,316 HCC and 1,665 non-tumorous tissue samples. These samples were obtained from 30 studies generated by mainly four types of profiling techniques (Affymetrix, Illumina, Agilent, and High-throughput sequencing), which are implemented in a wide range of platforms. Firstly, we scrutinized overlapping 26 genes that are differentially expressed in numerous datasets. Subsequently, we identified a panel of three genes (FCN3, CLEC1B, and PRC1) as HCC biomarker using different feature selection techniques. Three-genes-based HCC biomarker identified HCC samples in training/validation datasets with an accuracy between 93 and 98%, Area Under Receiver Operating Characteristic curve (AUROC) in a range of 0.97 to 1.0. A reasonable performance, i.e., AUROC 0.91-0.96 achieved on validation dataset containing peripheral blood mononuclear cells, concurred their non-invasive utility. Furthermore, the prognostic potential of these genes was evaluated on TCGA-LIHC and GSE14520 cohorts using univariate survival analysis. This analysis revealed that these genes are prognostic indicators for various types of the survivals of HCC patients (e.g., Overall Survival, Progression-Free Survival, Disease-Free Survival). These genes significantly stratified high-risk and low-risk HCC patients (p-value <0.05). In conclusion, we identified a universal platform-independent three-genes-based biomarker that can predict HCC patients with high precision and also possess significant prognostic potential. Eventually, we developed a web server HCCpred based on the above study to facilitate scientific community (http://webs.iiitd.edu.in/raghava/hccpred/).

Identification of Potential Serum Protein Biomarkers and Pathways for Pancreatic Cancer Cachexia Using an Aptamer-Based Discovery Platform

“…As well, IL-33 and the decoy receptor soluble IL1RL1/ST2 as measured by ELISA associates with poor survival in patients with advanced PDAC undergoing chemotherapy [ 36 ]. CLEC1B has been identified as a potential biomarker of hepatocellular carcinoma [ 37 , 38 ], but to date there are no data in PDAC. Four additional proteins were present in PDAC but did not associate to cachexia status, including GDF15, TIMP1, CCL22, and APP.…”

Integrative multi-omics approach for stratification of tumor recurrence risk groups of Hepatocellular Carcinoma patients

“…Hence, it is vital to elucidate the omics signature or biomarkers for tumor recurrence in HCC. The availability of omics data of liver cancer patients facilitates the research in discovering the robust signatures for liver cancer [52,[81][82][83]; hence, multi-omics data can provide a holistic picture of this malignacy. Thus, in the present study, we have made an attempt to explore the multi-omics layers of HCC patients (obtained from public repositories, i.e., GDC data portal and GEO) for the elucidation of signatures that can significantly stratify the recurrence risk groups.…”

Systematic Analysis of the Transcriptome Profiles and Co-Expression Networks of Tumour Endothelial Cells Identifies Several Tumour-Associated Modules and Potential Therapeutic Targets in Hepatocellular Carcinoma

“…Tumour endothelial cells play a decisive role in cancer progression. Numerous studies have been undertaken to investigate the underlying biological processes and identify prognostic markers or therapeutic targets in HCC and other tumour entities [ 36 , 65 , 66 , 67 , 68 ]. However, except for the dataset used in this study (GSE51401), these studies are of too limited size to allow the systematic integration of differential gene expression and pathway perturbation analysis with advanced bioinformatics approaches.…”