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Dry January: How abstinence for a month could help reduce the risk of developing liver cancer

Dry January: How abstinence for a month could help reduce the risk of developing liver cancer

Happy new year everyone! After the festive season full of delicious food and mulled wine in December, it’s time to slow down during Dry January! Dry January is a public health campaign to encourage people to control their alcohol consumption by stopping the consumption of alcohol for a month. The origins of this campaign date back to 1940s, when the Finnish government first offered “Sober January” as part of their war effort (1). Then years later, “Dry January” was registered as a trademark by the UK based charity called Alcohol Concern in 2013 and 4,000 British people took part in it. Since then, the impact of the campaign has massively improved and in 2022, 130,000 people from UK officially signed-up for the movement (2).

Alcohol abstinence for a short period is considered as a healthy break and can help to reduce liver fat, cholesterol, blood sugar, blood pressure, body weight, as well as number of cancer-related proteins in the blood (3). Besides, it’s an excellent chance to boost your energy and mood, improve concentration, save some money and much more. This campaign helped numerous people around the world to control their drinking habits long term, develop a healthier relationship with alcohol and even to stay completely abstinent.

Excessive alcohol intake is the main risk factor of alcoholic liver disease development, which could potentially progress to cirrhosis (severe scarring of the liver) if the person doesn’t stop drinking, and then progress to liver cancer, mainly hepatocellular carcinoma (HCC), at the latest stage (4). In the US, chronic liver disease and cirrhosis was the 4th leading cause of death among women and 5th among men aged 40 to 59 in 2019 (5). Even though there is a decline in liver cancer incidences in recent years, 41,260 new cases (2% of all cancer cases) and 30,530 deaths (5% of all cancer deaths) are still estimated for liver and intrahepatic bile duct cancers in US in 2022 (5).

It is known that liver cancer has high tumor heterogeneity, both inter- and intratumoral heterogeneity (6). The latest advancements in single-cell sequencing (sc-seq) methods play a huge role in the characterization of heterogenous tumor microenvironment (TME) of HCC. Currently, researchers can differentiate individual cell types and cell subgroups of HCC tissues, including malignant cells, various types of immune cells and stromal cells. In fact, immune cells in the TME and their cell-cell interactions and crosstalk play a crucial role in tumor immunosuppression, where researchers are getting closer in deciphering those interactions thanks to emerging sc-seq technologies (6).

The application of sc-seq technologies to liver cancer studies started in 2016, when Hou et al. first used scTrio-seq multi-omics method to only 25 single cancer cells of an HCC sample and successfully identified two cell subpopulations, one of which composed of cancer cells with more invasive markers and more likely to escape immune recognition (7, 8). Then, Zhen et al. used Smart-seq scRNA-seq on liver cancer stem cell (CSC) lines and revealed the biological and transcriptome heterogeneity of CSC subpopulations in HCC for the first time (7, 9). In 2020, Zhang et al. performed scRNA-seq on 56,871 cells of eight intrahepatic cholangiocarcinoma (ICC) tissues and identified four main subclusters of tumor cells based on their CNVs and DEGs and observed high degree of intertumor heterogeneity (7, 10). Later, a spatial transcriptomics approach was used by Wu et al. to study genome-wide TME characteristics and they identified the importance of complete fibrous capsule for both TME structure and intratumor heterogeneity (7, 11).

To study TMEs and the crosstalk between different immune and tumor cells, Zheng et al. applied scRNA-seq on 5,063 T cells isolated from peripheral blood, tumor samples, and neighboring non-cancerous tissues from six patients with HCC and identified 11 distinct T cell subsets based on their molecular and functional properties. They also provided important evidence for the differential distribution of CD8+ T cells as a feature in the TME of HCC (7, 12). In a later study conducted by Zheng B et al., the spatial heterogeneity of the immune microenvironment in HCC was investigated and an increased number of CD4+ effector memory T cells were observed when moving from the non-tumor region to the tumor core, while the opposite trend was observed for CD8+ effector memory T cells (7, 13). Furthermore, they observed enriched tumor-infiltrating exhausted T cells and regulatory T cells (Tregs) in HCC TMEs. In 2021, Sun et al. performed scRNA-seq on 16,498 cells to compare primary and early-relapse HCC cases and detected a distinct immune ecosystem in early-relapse HCC samples (7, 14). They also detected a unique subgroup of CD8+T cells with high expression of CD161, low clonal expansion, innate-like dysfunctional cytotoxicity and enrichment of these cells were associated with a worse prognosis (7, 14).

Figure 1. tSNE plot for 40 T cell clusters identified via FlowSOM clustering (left) and tSNE plot for identified classic T cell clusters (right) (image from (13),

These are several examples of studies using different single-cell technologies to solve the mystery of liver cancer heterogeneity and the dynamics of the immune cells in tumor microenvironments at a single cell level. Therefore, it is evident that single cell technologies are quickly evolving to identify more patterns of liver cells which may lead to discovery of potential therapeutic targets for cancer precision medicine and development of novel treatment strategies for related diseases in the near future.


  5. Siegel et al. Cancer statistics. CA Cancer J. Clin, 2022, 10.3322/caac.21708
  6. Zhang QY, Ho DW, Tsui YM, Ng IO. Single-Cell Transcriptomics of Liver Cancer: Hype or Insights? Cell Mol Gastroenterol Hepatol. 2022;14(3):513-525. doi: 10.1016/j.jcmgh.2022.04.014. Epub 2022 May 14. PMID: 35577269; PMCID: PMC9294331.
  7. Tian B, Li Q. Single-Cell Sequencing and Its Applications in Liver Cancer. Front Oncol. 2022 Apr 21;12:857037. doi: 10.3389/fonc.2022.857037. PMID: 35574365; PMCID: PMC9097917.
  8. Hou Y, Guo H, Cao C, Li X, Hu B, Zhu P, et al. Single-Cell Triple Omics Sequencing Reveals Genetic, Epigenetic, and Transcriptomic Heterogeneity in Hepatocellular Carcinomas. Cell Res (2016) 26(3):304–19. doi: 10.1038/cr.2016.23
  9. Zheng H, Pomyen Y, Hernandez MO, Li C, Livak F, Tang W, et al. Single-Cell Analysis Reveals Cancer Stem Cell Heterogeneity in Hepatocellular Carcinoma. Hepatology (2018) 68(1):127–40. doi: 10.1002/hep.29778
  10. Zhang M, Yang H, Wan L, Wang Z, Wang H, Ge C, et al. Single-Cell Transcriptomic Architecture and Intercellular Crosstalk of Human Intrahepatic Cholangiocarcinoma. J Hepatol (2020) 73(5):1118–30. doi: 10.1016/j.jhep.2020.05.039
  11. Wu R, Guo W, Qiu X, Wang S, Sui C, Lian Q, et al. Comprehensive Analysis of Spatial Architecture in Primary Liver Cancer. Sci Adv (2021) 7(51): eabg3750. doi: 10.1126/sciadv.abg3750
  12. Zheng C, Zheng L, Yoo JK, Guo H, Zhang Y, Guo X, et al. Landscape ofInfiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing. Cell (2017) 169(7):1342–56.e16. doi: 10.1016/j.cell.2017.05.035
  13. Zheng B, Wang D, Qiu X, Luo G, Wu T, Yang S, et al. Trajectory and Functional Analysis of PD-1(High) CD4(+)CD8(+) T Cells in Hepatocellular Carcinoma by Single-Cell Cytometry and Transcriptome Sequencing. Adv Sci (Weinh) (2020) 7(13):2000224. doi: 10.1002/advs.202000224
  14. Sun Y, Wu L, Zhong Y, Zhou K, Hou Y, Wang Z, et al. Single-Cell Landscape of the Ecosystem in Early-Relapse Hepatocellular Carcinoma. Cell (2021) 184 (2):404–21 e16. doi: 10.1016/j.cell.2020.11.041