image: (A) Unsupervised consistent clustering of the two subtypes of TCGA-HCC patients. (B) Comparison of overall survival (OS) between the two subtypes of TCGA-HCC patients. (C) Comparison of MIAS scores between the two subtypes of TCGA-HCC patients. (D) Comparison of GEP scores between the two subtypes of TCGA-HCC patients. (E) Comparison of ESTIMATE-immunescore scores between two subtypes of TCGA-HCC patients. (F) Comparison of IMPRES scores between two subtypes of TCGA-HCC patients. (G) Comparison of IPS scores between two subtypes of TCGA-HCC patients. (H) Comparison of xCell-immunescore scores between two subtypes of TCGA-HCC patients. (I) Distribution of TIDE scores between responders and non-responders in TCGA-HCC patients predicted by the TIDE algorithm. (J) Comparison of response rates between the two subtypes of TCGA-HCC patients predicted by the TIDE algorithm. (K) Comparison of TIDE scores between the two subtypes of TCGA-HCC patients. (L) Unsupervised consistency clustering of the two subtypes of IMvigor210 cohort. (M) Comparison of response rates between the two subtypes of IMvigor210 cohort. (N) Comparison of survival rates between the two subtypes of the IMvigor210 cohort.
Credit: Genes & Diseases
Hepatocellular carcinoma (HCC), the most common type of primary liver cancer, accounts for about 75–85% of all liver cancer cases. Immune checkpoint inhibitors (ICIs), despite yielding a superior therapeutic impact, have substantial drawbacks, including therapy-resistant patients and absence of biomarkers for predicting the response to ICIs. Hence, there is an urgent need to investigate dependable biomarkers to enhance patient prognosis while minimizing the adverse effects of ICIs.
This research, published in the Genes & Diseases journal by a team from Chongqing Medical University, explores the heterogeneity of immune subtypes at the single-cell level using bulk and single-cell sequencing to identify potential ICI response-associated cells and therapeutic agents in HCC.
The researchers employed seven predictive scores related to ICI response to measure the effectiveness of an immune gene set in categorizing HCC patients. The results confirmed the ability of immune signature scores to distinguish two immune subtypes, subtype 1 and subtype 2, with significant differences in immune response. Patients in both subtypes showed varying overall survival, immunity levels, biological activities, and TP53 mutation rates. Furthermore, the study revealed that patients with subtype 1 demonstrated significantly improved overall survival rates and higher immune response scores compared to those with subtype 2.
Interestingly, this study noted that only subtype 1-associated natural killer (NK) cells showed a positive correlation with immune-promoting scores, highlighting their potential role in enhancing ICI treatment efficacy. In addition, the researchers screened 2494 potential drugs using multiple databases and network approaches to identify potential therapeutic agents targeting subtype 1-associated NK cells. Among the candidates, docetaxel and thalidomide emerged as promising options for enhancing ICI response. Notably, sensitivity analysis revealed that docetaxel sensitivity in HCC patients rose as the levels of subtype 1-related NK cells increased, suggesting that increased sensitivity to docetaxel may enhance immune responses in HCC patients.
Although this research provides a framework for immune-based classification in HCC, the correlation between NK cell subsets and docetaxel sensitivity requires further validation through clinical trials. Nevertheless, the combination of subtype 1-associated NK cells and docetaxel may offer new hope for ICI treatment in HCC. In conclusion, this study has revealed the cell types that potentially affect ICIs and identified potential drugs by combining bulk sequencing and single-cell sequencing, which will provide a scientific reference for future studies of ICIs in HCC treatment.
Reference
Title of Original Paper - Comprehensive analysis of immune subtype characterization on identification of potential cells and drugs to predict response to immune checkpoint inhibitors for hepatocellular carcinoma
DOI: https://doi.org/10.1016/j.gendis.2024.101471
Journal: Genes & Diseases
Genes & Diseases is a journal for molecular and translational medicine. The journal primarily focuses on publishing investigations on the molecular bases and experimental therapeutics of human diseases. Publication formats include full length research article, review article, short communication, correspondence, perspectives, commentary, views on news, and research watch.
# # # # # #
Genes & Diseases publishes rigorously peer-reviewed and high quality original articles and authoritative reviews that focus on the molecular bases of human diseases. Emphasis is placed on hypothesis-driven, mechanistic studies relevant to pathogenesis and/or experimental therapeutics of human diseases. The journal has worldwide authorship, and a broad scope in basic and translational biomedical research of molecular biology, molecular genetics, and cell biology, including but not limited to cell proliferation and apoptosis, signal transduction, stem cell biology, developmental biology, gene regulation and epigenetics, cancer biology, immunity and infection, neuroscience, disease-specific animal models, gene and cell-based therapies, and regenerative medicine.
Scopus CiteScore: 7.3 | Impact Factor: 6.9
# # # # # #
More information: https://www.keaipublishing.com/en/journals/genes-and-diseases/
Editorial Board: https://www.keaipublishing.com/en/journals/genes-and-diseases/editorial-board/
All issues and articles in press are available online in ScienceDirect (https://www.sciencedirect.com/journal/genes-and-diseases).
Submissions to Genes & Disease may be made using Editorial Manager (https://www.editorialmanager.com/gendis/default.aspx ).
Print ISSN: 2352-4820
eISSN: 2352-3042
CN: 50-1221/R
Contact Us: editor@genesndiseases.com
X (formerly Twitter): @GenesNDiseases (https://x.com/GenesNDiseases)
Journal
Genes & Diseases