Background: Cancer stem cells (CSCs) are able to survive after cancer therapies, resulting in tumor progression and recurrence, as is seen in colorectal cancer. Therapies targeting CSCs are regarded as novel and promising strategies for efficiently eradicating tumors. Berberine, an isoquinoline alkaloid extracted from the Chinese herbal medicine Coptis chinensis, was found to have antitumor activities against colorectal cancer, without knowing whether it exerts inhibitory effects on colorectal CSCs and the potential mechanisms.

Methods: In this study, we examined the inhibitory roles of Berberine on CSCs derived from HCT116 and HT29 by culturing in serum-free medium. We also examined the effects of Berberine on m⁶A methylation via regulating fat mass and obesity-associated protein (FTO), by downregulating β-catenin.

Results: We examined the effects of Berberine on the tumorigenicity, growth, and stemness of colorectal cancer stem-like cells. The regulatory effect of Berberine on N6-methyladenosine (m⁶A), an abundant mRNA modification, was also examined. Berberine treatment decreased cell proliferation by decreasing cyclin D1 and increasing p27 and p21 and subsequently induced cell cycle arrest at the G₁/G₀ phase. Berberine treatment also decreased colony formation and induced apoptosis. Berberine treatment transcriptionally increased FTO and thus decreased m⁶A methylation, which was reversed by both FTO knockdown and the addition of the FTO inhibitor FB23-2. Berberine induced FTO-related decreases in stemness in HCT116 and HT29 CSCs. Berberine treatment also increased chemosensitivity in CSCs and promoted chemotherapy agent-induced apoptosis. Moreover, we also found that Berberine treatment increased FTO by decreasing β-catenin, which is a negative regulator of FTO.

Conclusions: Our observation that Berberine effectively decreased m⁶A methylation by decreasing β-catenin and subsequently increased FTO suggests a role of Berberine in modulating stemness and malignant behaviors in colorectal CSCs.

Many diseases are accompanied by changes in certain biochemical indicators called biomarkers in cells or tissues. A variety of biomarkers, including proteins, nucleic acids, antibodies, and peptides, have been identified. Tumor biomarkers have been widely used in cancer risk assessment, early screening, diagnosis, prognosis, treatment, and progression monitoring. For example, the number of circulating tumor cell (CTC) is a prognostic indicator of breast cancer overall survival, and tumor mutation burden (TMB) can be used to predict the efficacy of immune checkpoint inhibitors. Currently, clinical methods such as polymerase chain reaction (PCR) and next generation sequencing (NGS) are mainly adopted to evaluate these biomarkers, which are time-consuming and expansive. Pathological image analysis is an essential tool in medical research, disease diagnosis and treatment, functioning by extracting important physiological and pathological information or knowledge from medical images. Recently, deep learning-based analysis on pathological images and morphology to predict tumor biomarkers has attracted great attention from both medical image and machine learning communities, as this combination not only reduces the burden on pathologists but also saves high costs and time. Therefore, it is necessary to summarize the current process of processing pathological images and key steps and methods used in each process, including: (1) pre-processing of pathological images, (2) image segmentation, (3) feature extraction, and (4) feature model construction. This will help people choose better and more appropriate medical image processing methods when predicting tumor biomarkers.