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Home / Journals / BIOCELL / Online First / doi:10.32604/biocell.2024.055848
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ARTICLE

Unveiling the predictive power of bacterial response-related genes signature in hepatocellular carcinoma: with bioinformatics analyses and experimental approaches

ATIEH POURBAGHERI-SIGAROODI1, MAJID MOMENY2, NIMA REZAEI3,4,5, FATEMEH FALLAH1,*, DAVOOD BASHASH6,*
1 Pediatric Infections Research Center, Research Institute for Children’s Health, Shahid Beheshti University of Medical Sciences, Tehran, 15468-15514, Iran
2 Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, 1461884513, Iran
3 Research Center for Immunodeficiencies, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, 1461884513, Iran
4 Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, 1461884513, Iran
5 Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, 1461884513, Iran
6 Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, 1985717443, Iran
* Corresponding Author: FATEMEH FALLAH. Email: email; DAVOOD BASHASH. Email: email
(This article belongs to the Special Issue: Gut Microbiota in Human Health: Exploring the Complex Interplay)

BIOCELL https://doi.org/10.32604/biocell.2024.055848

Received 08 July 2024; Accepted 02 October 2024; Published online 24 October 2024

Abstract

Background: Despite progress in therapeutic strategies, treatment failure in hepatocellular carcinoma (HCC) remains a major challenge, resulting in low survival rates. The presence of bacteria and the host’s immune response to bacteria can influence the pathogenesis and progression of HCC. We developed a risk model based on bacterial response-related genes (BRGs) using gene sets from molecular signature databases to identify new markers for predicting HCC outcomes and categorizing patients into different risk groups. Methods: The data from the Cancer Genome Atlas (TCGA) portal was retrieved, and differentially expressed BRGs were identified. Uni- and multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) LASSO analyses were executed to develop the prognostic risk model. Key contributor to the prognostic model was identified, and the results were tested by using experimental assays in HCC cell lines. Results: Multivariate analysis demonstrated an independent prognostic factor of 12-BRG signature in HCC patients. The low-risk group had better overall survival with significantly lower tumor mutation burden (TMB). The risk scores were negatively correlated with the presence of tumor-infiltrating immune cells. In an effort to find the key contributor of the 12-BRG signature, we found polo like kinase1 (PLK1) had the best accuracy with 1-, 3-, and 5-year AUC of 0.72, 0.66, and 0.65, respectively. Both PLK1 inhibitor Volasertib and the knockdown of the PLK1 gene resulted in diminished viability in HCC cell lines. The combination of PLK1 inhibition with low-dose chemotherapy exhibited an amplified effect of the treatment. Conclusion: To date, there have been no reports of BRG biomarkers in HCC, and this study represents for the first time that a 12-BRG signature has the potential to predict the survival of HCC.

Keywords

Hepatocellular carcinoma; Bacterial response-related signature; Tumor microenvironment; Bioinformatics; Prognostic models

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