Researchers identified a sudden transcriptomic switch that turns healthy liver tissue cancerous


By combining RNA sequencing, bioinformatics and mathematical modeling, University of California San Diego School of Medicine and Moores Cancer Center researchers identified a sudden transcriptomic switch that turns healthy liver tissue cancerous.

The finding was used to develop a quantitative analytical tool that assesses cancer risk in patients with chronic liver disease and to predict tumor stages and prognosis for patients with liver cancer.

In the December 16, 2019 online edition of the Proceedings of the National Academy of Science (PNAS), Gen-Sheng Feng, Ph.D., professor of in the Department of Pathology and Section of Molecular Biology, Division of Biological Sciences at UC San Diego, and team describe developing a tumorigenic index score that identifies a shift from healthy to malignant cells.

“Because we do not have an effective drug to treat liver cancer in its late stages, early detection of liver cancer, when a tumor is less than 10 millimeters, allows oncologists to better treat, surgically remove and kill cancer cells,” said Feng, senior author on the paper.

“For the first time, we have a mathematical equation that can predict when healthy liver cells become cancerous and, importantly, we are able to detect cancer cells before tumors are visible in a standard clinical setting.”

The new analytical tool focused on the analysis of transcription factor clusters. Transcription factors are proteins that bind to specific DNA sequences in order to direct which genes should be turned on or off in a cell.

By quantitatively measuring changes in transcription factors together with downstream target genes as a unit (transcription factor clusters), the research group interrogated RNA-sequencing data collected in the pre-cancer and cancer stages of mouse models with different forms of liver cancer and chronic liver diseases like steatosis, fibrosis and cirrhosis.

The analysis found 61 transcription factor clusters that were either up- or down-regulated in mice with cancer, even identifying transcription factors that have not been previously reported in liver cancer.

Gaowei Wang, Ph.D., a computational biologist and postdoctoral fellow in Feng’s lab, helped design a comprehensive analysis of a liver cell transcriptome – the entire collection of RNA sequences in a cell.

This allowed the team to compare expression of transcription factor clusters in healthy livers and those with chronic liver diseases at various stages to identify when cells became cancerous in mice.

After developing the math model using mouse data, researchers applied the analytical tool to a public database to re-analyze human patient data and were able to identify which people had cancer and which had chronic liver disease.

In patients with cirrhosis, who are at high risk of developing cancer, they could see a positive tumor index score and in some cases tumor nodules that were not yet visible in the clinic.

“This mathematical approach can be developed into a risk assessment and early diagnostic tool of liver cancer development for a larger population of people living with chronic liver disease, particularly those with cirrhosis,” said Feng.

“The analysis of individuals at high risk may have an important application in precision medicine. And, with further development and optimization, this tool might be modified to predict the development of other cancers.”

According to the American Cancer Society, more than 700,000 new cases of liver cancer are diagnosed globally and 600,000 deaths occur each year, making it among the leading causes of cancer death in the world.

In 2019, an estimated 42,000 new cases of liver cancer will be diagnosed and 31,000 people will die in the United States alone.

Further testing is needed before it can be used in a clinical setting. The next step is to analyze liver biopsies, with the ultimate goal of using blood samples to predict risk and stage liver cancer, said Feng.

Hepatocellular carcinoma (HCC) is the cancer with the second highest mortality rate worldwide [1]. It is generally associated with risk factors such alcohol consumption and aflatoxin B1 exposition [2]. HCC occurrence is still rising even in developed countries where it is linked with obesity, diabetes, and hepatitis B and C virus (HBV and HCV) infection [3].

The poor survival rate of HCC patients (1 and 5-year survival rate of 44% and 17%, respectively [4]) is partly due to limited treatments options and their unsatisfactory efficacy. Liver transplantation or chirurgical resection of the tumor are the only curative treatments [5]. However, since HCC diagnosis is generally tardive, more than 80% of patients are not eligible and chemoembolization or drugs have to be used.

Nonetheless, these treatments have limited efficacy at advanced tumor stages [6]. Hence, early diagnosis of patients for hepatocellular carcinoma is crucial.

HCC screening is generally made by imagery techniques, such as ultrasound or computed tomography (limited to tumor bigger than 1 cm), or by assessing the alpha-fetoprotein (AFP) serum levels [78]. AFP is a glycoprotein produced by fetal yolk sac and liver, and its concentration decreases rapidly after birth. Some conditions, like pregnancy or cancer, can generate high AFP levels in serum [9].

AFP level can give information about HCC since it is positively correlated with HBV infection, tumor size, low cellular differentiation, and reaches the highest level in the case of metastatic tumor [1012]. However, its low expression in early stage cancers makes it a poor biomarker for large-scale HCC screening, especially since 30% of patients with HCC continuously have normal AFP levels [13].

Other pathologies such as cirrhosis and stomach cancer also have elevated AFP levels which contribute to the relative non-specificity for HCC detection [14]. This leads to sensitivity levels which vary from 55% to 61%, and specificity levels of 78% to 91% when a 20 ng/mL level cut-off is used for HCC screening [15].

The low sensibility and high false-positive rates of AFP as marker therefore justify the identification of better biomarkers for HCC screening. The development of new technologies, notably in genomics, allows characterization of molecular events involved in carcinogenesis, including mRNA expression levels in tissue samples.

The Cancer Genome Atlas (TCGA) research network recently overviewed HCC and normal liver tissues data obtained from multiple genomic platforms. They identified important characteristics of HCC such as significantly mutated genes (e.g. CTNNB1A, TP53, TERT promoter), different promoter methylation profiles (hypermethylation of CDKN2A which causes gene silencing), and key pathways affected in HCC (WNT, SHH, RTK/KRAS, chromatin remodeling and metabolic programming) [3].

In the current large-scale study, RNA sequencing data from the TCGA were used to identify differentially expressed genes in more than 200 HCC tissues. Our study primarily focuses on members of the cytochrome P450 family in order to find potential biomarkers.


Advances in genomic technologies have allowed the characterization of molecular events occurring during carcinogenesis, such as mRNA expression dysregulation, which could potentially lead to the identification of new biomarkers. In the case of HCC, differential gene expression levels of various CYP450s have previously been reported [2326].

However, these previous studies were generally performed on a limited number of HCC tissue samples or focused on a small subset on CYP450s. In contrast, in the present study focusing on the entire CYP450 family (57 CYP450 genes), we monitored the expression landscape of CYP450s in more than 200 HCC tissues using RNA-Seq data from The Cancer Genome Atlas. Following rigorous statistical and validation assays, 3 potential biomarkers for HCC were identified: CYP1A2, CYP2B6, and CYP2C19. Our analysis also demonstrated that the observed change of expression for these three genes is relatively constant from stage I to stage IV, suggesting that the identified CYP450s could be suitable biomarkers for early identification of HCC.

Various members of the CYP450s family were previously identified as interesting for cancer screening or treatments. For instance, CYP2J2 and CYP2W1 were both found to have a higher level of expression in carcinoma cells and transformed tissues where they could have a role in the progression or treatment of cancers [2027].

CYP1B1 is one of the best-known CYP450s up-regulated in multiple cancers, like breast, colon and brain cancer [28], and studies are in progress to use it as a therapeutic target in the treatment of cancers [29]. Moreover, CYP17A1 has also been extensively characterized, both at the mRNA and protein levels, in tissues and sera of HCC patients. A previous study showed that a threshold of 60.2ng/mL allows identification of HCC patients with a sensitivity and specificity of 86.9% and 76.8% respectively, while a combination with AFP achieved 90.1% and 80.3% of sensitivity and specificity [30].

In addition to the identification of CYP450s as markers for HCC, the roles of CYP450s in carcinogenesis merits further investigation. Indeed, CYP1A2, CYP2B6, and CYP2C19, which are repressed in HCC, are involved in the metabolism of eicosanoids, drugs, and foreign chemicals [17].

This could potentially promote HCC development by an accumulation of toxic compounds for cells. Interestingly, CYP1A2 is the major CYP450 found in the liver and is involved in the metabolism of 8.9% of drugs used in the clinic, while CYP2B6 and CYP2C19 are involved in the metabolism of 7.2% and 6.8%, respectively of these drugs [31].

The lower levels of these enzymes in HCC patients could then have an impact on their susceptibility to drugs doses or the activation of pro-drugs. Similarly, CYP1B1 is generally absent from normal adult liver and its expression is associated with carcinogenesis, partly because it can activate pro-carcinogens [32]. On the other hand, CYP26A1, which is repressed in HCC, is involved in retinoic acid inactivation [17].

It is therefore possible that products, such as retinoic acid, would have a role in the progression of HCC since a previous study revealed that vitamin A deficiency, which could result from a CYP26A1 depletion, is associated with increased susceptibility to carcinogenesis [33]. Finally, in hormone-dependent prostate and breast cancers, CYP17A1 and CYP19A1 are targeted by inhibitors for cancer treatments [3435]. In the case of HCC, different studies showed that a high level of estrogens would be protective for patients, which can correlate with the fact that HCC is much more present in men than women [3637].

Several studies are seeking the identification of new HCC biomarkers. Some of these potential markers such as AFP lectin-bound (AFP-L3) [38], Des-γ-carboxy prothrombin [39], or glypican-3 (GPC3) [40], have interesting diagnosis performances.

Despite a large number of promising molecules, individual markers generally lack sensitivity and/or specificity to be sufficiently effective. The future of HCC screening will most likely involve the use of a combination of biomarkers based on various macromolecules such as mRNAs, proteins, mi-RNAs, or even powerful imagery techniques such as ultrasonography.

More information: Gaowei Wang et al, A tumorigenic index for quantitative analysis of liver cancer initiation and progression, Proceedings of the National Academy of Sciences (2019). DOI: 10.1073/pnas.1911193116


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