Reference: Kaur H, Dhall A, Kumar R and Raghava GPS (2020) Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma Using Large-Scale Transcriptomics Data. Front. Genet. 10:1306. doi: 10.3389/fgene.2019.01306

A webserver to predict Hepatocellular carcinoma (HCC)

HCCpred is a web-bench for the prediction of tumorous and non-tumorous Hepatocellular Carcinoma (HCC) patients. Our major prediction modules based on the robust biomarkers such as 3-Gene HCC Biomarker, 4-Gene HCC Biomarker, 5-Gene HCC Biomarker. These HCC biomarkers identified using gene expression profiles of a total of 3,961 samples include 2,306 HCC and 1,655 non-tumorous samples. The datasets derived from various profiling platforms such as Affymatrix, Illumina, High-througput and Agilent. The user can also analyse the expression pattern of any of 26 "core genes of HCC" in cancerous vs normal conditions.


This tool allow the user to predict whether the sample is Normal or cancerous using their expression profiles (RNA-seq expression data) of specific signature genes or biomarker of HCC. To predict the status of sample user need to provide expression values of specific genes. HCCpred incorporate three major models based on the robust diagonostic HCC biomarkers such as: I) 3-Gene HCC biomarker II) 4-Gene HCC biomarker III) 5-Gene HCC biomarkers.


This tool facilitates the user to evaluates the significant role and expression pattern of the "Core genes of HCC biomarkers". Further, it gives a threshold based value of each gene and mean expression values to discriminate Normal or Hepatocellular carcinoma (HCC) patients on the basis of datasets used in this study.

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