Tumor‐adjacent tissue co‐expression profile analysis reveals pro‐oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma

Abstract

Currently, molecular markers are not used when determining the prognosis and treatment strategy for patients with hepatocellular carcinoma (HCC). In the present study, we proposed that the identification of common pro‐oncogenic pathways in primary tumors (PT) and adjacent non‐malignant tissues (AT) typically used to predict HCC patient risks may result in HCC biomarker discovery. We examined the genome‐wide mRNA expression profiles of paired PT and AT samples from 321 HCC patients. The workflow integrated differentially expressed gene selection, gene ontology enrichment, computational classification, survival predictions, image analysis and experimental validation methods. We developed a 24‐ribosomal gene‐based HCC classifier (RGC), which is prognostically significant in both PT and AT. The RGC gene overexpression in PT was associated with a poor prognosis in the training (hazard ratio = 8.2, P = 9.4 × 10−6) and cross‐cohort validation (hazard ratio = 2.63, P = 0.004) datasets. The multivariate survival analysis demonstrated the significant and independent prognostic value of the RGC. The RGC displayed a significant prognostic value in AT of the training (hazard ratio = 5.0, P = 0.03) and cross‐validation (hazard ratio = 1.9, P = 0.03) HCC groups, confirming the accuracy and robustness of the RGC. Our experimental and bioinformatics analyses suggested a key role for c‐MYC in the pro‐oncogenic pattern of ribosomal biogenesis co‐regulation in PT and AT. Microarray, quantitative RT‐PCR and quantitative immunohistochemical studies of the PT showed that DKK1 in PT is the perspective biomarker for poor HCC outcomes. The common co‐transcriptional pattern of ribosome biogenesis genes in PT and AT from HCC patients suggests a new scalable prognostic system, as supported by the model of tumor‐like metabolic redirection/assimilation in non‐malignant AT. The RGC, comprising 24 ribosomal genes, is introduced as a robust and reproducible prognostic model for stratifying HCC patient risks. The adjacent non‐malignant liver tissue alone, or in combination with HCC tissue biopsy, could be an important target for developing predictive and monitoring strategies, as well as evidence‐based therapeutic interventions, that aim to reduce the risk of post‐surgery relapse in HCC patients.

4‐1BB Delineates Distinct Activation Status of Exhausted Tumor‐Infiltrating CD8+ T Cells in Hepatocellular Carcinoma

“…Then, the enrichment score for the gene signatures was calculated by GSVA . Gene expression and survival data of an independent HCC cohort were obtained from GSE 76427 and analyzed in the same manner. To conceptually compare the survival outcomes of the subcohorts, we used the enrichment score cutoff determined by the maximal chi‐square method using the R package, Maxstat, for each cohort …”

Cytoskeleton-associated membrane protein�4 is upregulated in tumor tissues and is associated with clinicopathological characteristics and prognosis in hepatocellular carcinoma

“…Tissue sample data sources. Three gene expression profile datasets of HCC tumor and adjacent tissues, GSE76427 (24), GSE84402 (25) and GSE57957 (26), were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/) ( Fig. 1) and analyzed using software packages in RStudio 3.6.1 version (https://rstudio.com/).…”

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

“…In this study, we extract raw expression data of 30 datasets, where 29 transcriptome datasets were obtained from GEO and one is from TCGA; each dataset contains at least 10 samples. The following is the list of datasets obtained from GEO: GSE102079 (Chiyonobu et al, 2018), GSE22405, GSE98383 (Diaz et al, 2018), GSE84402 (Wang et al, 2017), GSE64041 (Makowska et al, 2016), GSE69715 (Sekhar et al, 2018), GSE51401, GSE62232 (Schulze et al, 2015), GSE45267 (Chen et al, 2018a), GSE32879 (Oishi et al, 2012), GSE19665 (Deng et al, 2010), GSE107170 (Diaz et al, 2018), GSE76427 (Grinchuk et al, 2018), GSE39791 (Kim et al, 2014), GSE57957 (Mah et al, 2014), GSE87630 (Woo et al, 2017), GSE46408, GSE57555 (Murakami et al, 2015), GSE54236 (Villa et al, 2016;Zubiete-Franco et al, 2019), GSE65484 (Dong et al, 2015), GSE31370 (Seok et al, 2012), GSE84598, GSE89377, GSE29721 (Stefanska et al, 2011), GSE14323 (Mas et al, 2009), GSE25097 (Lamb et al, 2011;Tung et al, 2011;Wong et al, 2016), GSE14520 (Roessler et al, 2010;Zhao et al, 2015), GSE36376 (Lim et al, 2013), GSE36076). All GEO datasets were obtained using GEOquery package of Bioconductor in R-3.5.3.…”