Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics

Multistage carcinogenesis in occupational cholangiocarcinoma: the impact of clonal expansion and risk estimation

BACKGROUND

Both mutation induction and clonal expansion of mutated cells cause cancer. The probability of cancer development depends on mutations, clonal growth rates, and carcinogenic mechanisms. A recent study showed cases of occupational cholangiocarcinomas that originate multifocally, with higher mutation burden levels than those in common cholangiocarcinomas. This study aimed to identify the effect of clonal expansion on and estimate the risk of occupational and common intrahepatic cholangiocarcinomas (ICCs) using a multistage model modified to include the effect of cell expansion at any carcinogenic stage.

METHODS

The age-specific incidence of common ICC estimated from the Vital Statistics in Japan and the prognosis of ICC, and mutation frequencies of occupational and common ICC available from the previous report, were applied to a multistage model modified with cell proliferation effects. From the fittest model, the risk after exposure was estimated.

RESULTS

The required number of stages for carcinogenesis was estimated to be three based on the incidences and mutation frequencies of occupational and common ICCs. Based on this estimation, the predicted incidence curve under the model was similar to that estimated from the ICC mortality rate, except for older adults. The model indicated a minor effect of clonal expansion on the observed occupational ICC risk. It predicted a rapid decrease in ICC risk after the cessation of occupational exposure, although the time of clinical detection of cancer after the exposure was affected by latency. The model predicted an increase in cancer risk in older adults caused by cell expansion and common background mutations. However, the risk in older adults was overestimated in the case of common ICC; this divergence could influence occupational ICC cases.

CONCLUSIONS

Three-stage ICC carcinogenesis has been proposed. The high mutation burden levels caused by occupational exposure led to an immediate incidence of cancer. After a long period of relatively low cancer risk, an increased risk in older adults was also predicted.

Flow cytometry: Aspects and application in plant and biological science

Flow cytometry is a potent method that enables the quick and concurrent investigation of several characteristics of single cells in solution. Photodiodes or photomultiplier tubes are employed to detect the dispersed and fluorescent light signals that are produced by the laser beam as it passes through the cells. Photodetectors transform the light signals produced by the laser into electrical impulses. A computer then analyses these electrical impulses to identify and measure the various cell populations depending on their fluorescence or light scattering characteristics. Based on their fluorescence or light scattering properties, cell populations can be examined and/or isolated. This review covers the basic principle, components, working and specific biological applications of flow cytometry, including studies on plant, cell and molecular biology and methods employed for data processing and interpretation as well as the potential future relevance of this methodology in light of retrospective analysis and recent advancements in flow cytometry.

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.