Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses

Abstract

Background: Hepatocellular carcinoma (HCC) is considered the most common type of liver cancer and the fourth leading cause of cancer-related deaths in the world. Since the disease is usually diagnosed at advanced stages, it has poor prognosis. Therefore, reliable biomarkers are urgently needed for early diagnosis and prognostic assessment.Methods: We used genome-wide gene expression profiling datasets from human and rat early HCC (eHCC) samples to perform integrated genomic and network-based analyses, and discovered gene markers that are expressed in blood and conserved in both species. We then used independent gene expression profiling datasets for peripheral blood mononuclear cells (PBMCs) for eHCC patients and from The Cancer Genome Atlas (TCGA) database to estimate the diagnostic and prognostic performance of the identified gene signature. Furthermore, we performed functional enrichment, interaction networks and pathway analyses.Results: We identified 41 significant genes that are expressed in blood and conserved across species in eHCC. We used comprehensive clinical data from over 600 patients with HCC to verify the diagnostic and prognostic value of 41-gene-signature. We developed a prognostic model and a risk score using the 41-geneset that showed that a high prognostic index is linked to a worse disease outcome. Furthermore, our 41-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis. Our data reveals a number of cancer-related pathways and hub genes, including EIF4E, H2AFX, CREB1, GSK3B, TGFBR1, and CCNA2, that may be essential for eHCC progression and confirm our gene signature’s ability to detect the disease in its early stages in patients’ biological fluids instead of invasive procedures and its prognostic potential.Conclusion: Our findings indicate that integrated cross-species genomic and network analysis may provide reliable markers that are associated with eHCC that may lead to better diagnosis, prognosis, and treatment options.

Transcriptomic data analysis coupled with copy number aberrations reveals a blood-based 17-gene signature for diagnosis and prognosis of patients with colorectal cancer

“…We assessed the classifier's performance in terms of accuracy, specificity, sensitivity, and area under the curve (AUC), as described previously (Al-Harazi et al, 2021a;Al-Harazi et al, 2021b). The SVM with linear kernel has outperformed other algorithms and the 17-gene classifier achieved a high accuracy of 99 percent, and sensitivity, specificity and AUC of 99%, 100% and 99%, respectively (Figure 4B), confirming the 17-gene signature's ability to discriminate patients from normal controls.…”

“…First, we used the GSE23878 dataset for building the classification model, and then tested the classification performance on an indepedent dataset (TCGA dataset) to confirm if the 17-gene-classifier can distinguish patients from normal controls. We evaluated the performance of the classifier for its accuracy, specificity, sensitivity, and area under curve (AUC), as described previously (Al-Harazi et al, 2021a;Al-Harazi et al, 2021b). The analyses were performed using PARTEK Genomics Suite (Partek Inc., St. Lois, MO, United States).…”

“…Since tumorigenesis genes show associations with copy number variations (CNVs) and expression levels, it is possible to increase the diagnostic reliability as well as the predictive potential of prognosis by integrating CNV and gene expression data (Sheng et al, 2011;Miao et al, 2014;Shao et al, 2019;Kaya et al, 2022). Indeed, the previous studies, including our own, reported that the multiomics approach may increase the robustness and reliability of biomarkers associated with complex diseases, including cancer (Miao et al, 2014;Aldosary et al, 2020;Das et al, 2020;Al-Harazi et al, 2021a;Baloni et al, 2021;Kaya et al, 2022;Ruan et al, 2022). Additionally, it has been reported that networkbased approaches have high efficacy in identifying biomarkers for many complex diseases, including several different types of cancer (Wang et al, 2017;Chen et al, 2019;Liu et al, 2019;Uddin et al, 2019;Khan et al, 2020;Al-Harazi et al, 2021b).…”

Global Transcriptional Profiling of Granulosa Cells from Polycystic Ovary Syndrome Patients: Comparative Analyses of Patients with or without History of Ovarian Hyperstimulation Syndrome Reveals Distinct Biomarkers and Pathways

“…Several studies have indicated that GC dysfunction in women with PCOS may contribute to abnormal folliculogenesis [ 6 , 7 ]. Recent advances in omics technologies (genomics, transcriptomics, proteomics, and others) have enabled researchers to better understand the molecular characteristics of diseases and to identify disease biomarkers [ 7 , 8 , 9 , 10 , 11 ].…”

H2AFX might be a prognostic biomarker for hepatocellular carcinoma

“… 5 H2AFX is a potential regulator of DNA repair and is very important for DDR. 6 , 7 In some previous studies 8 , 9 H2AFX may help detect the transformation and progression of early HCC. H2AFX has been identified in various researches, including breast cancer, 10 gastric intestinal metaplasia, 11 and prostate cancer.…”