Unraveling molecular similarities between colorectal polyps and colorectal cancer: a systems biology approach.

Background/aims: Colorectal cancer (CRC) and colorectal polyps are intimately linked, with polyps acting as precursors to CRC. Understanding the molecular mechanisms governing their development is crucial for advancing diagnosis and treatment. Employing a systems biology approach, we investigated the molecular similarities between polyp and CRC.

Cell-cell communication in stem cells and cancer: Alone but in touch.

Background: Cellular communication and signaling pathways are fundamental regulators of stem cell and cancer cell behaviors. This review explores the intricate interplay of these pathways in governing cellular behaviors, focusing on their implications for diseases, particularly cancer.

Objectives: This comprehensive review aims to elucidate the significance of cellular signaling pathways in regulating the behavior of stem cells and cancer cells.

Identification of common factors among fibrosarcoma, rhabdomyosarcoma, and osteosarcoma by network analysis.

Sarcoma cancers are uncommon malignant tumors, and there are many subgroups, including fibrosarcoma (FS), which mainly affects middle-aged and older adults in deep soft tissues. Rhabdomyosarcoma (RMS), on the other hand, is the most common soft-tissue sarcoma in children and is located in the head and neck area. Osteosarcomas (OS) is the predominant form of primary bone cancer among young adults, primarily resulting from sporadically random mutations.

The Epigenetic Regulation of Quiescent in Stem Cells.

This review article discusses the epigenetic regulation of quiescent stem cells. Quiescent stem cells are a rare population of stem cells that remain in a state of cell cycle arrest until activated to proliferate and differentiate. The molecular signature of quiescent stem cells is characterized by unique epigenetic modifications, including histone modifications and deoxyribonucleic acid (DNA) methylation.

Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies.

Background: Breast cancer is a major public health concern, and early diagnosis and classification are critical for effective treatment. Machine learning and deep learning techniques have shown great promise in the classification and diagnosis of breast cancer.

Purpose: In this review, we examine studies that have used these techniques for breast cancer classification and diagnosis, focusing on five groups of medical images: mammography, ultrasound, MRI, histology, and thermography.

Zbp1 gene: a modulator of multiple aging hallmarks as potential therapeutic target for age-related diseases.

The Zbp1 gene has recently emerged as a potential therapeutic target for age-related diseases. Multiple studies have reported that Zbp1 plays a key role in regulating several aging hallmarks, including cellular senescence, chronic inflammation, DNA damage response, and mitochondrial dysfunction. Regarding cellular senescence, Zbp1 appears to regulate the onset and progression of senescence by controlling the expression of key markers such as p16INK4a and p21CIP1/WAF1.

Common factors among three types of cells aged in mice.

The greatest risk factor for the formation of numerous significant chronic disorders is aging. Understanding the core molecular underpinnings of aging can help to slow down the inevitable process. Systematic study of gene expression or DNA methylation data is possible at the transcriptomics and epigenetics levels.

Cancer stem cells as therapeutic targets of pancreatic cancer.

The discovery of stem cells and their potential abilities in self-renewal and differentiation has opened a new horizon in medicine. Scientists have found a small population of stem cells in some types of cancers with the same functions as normal stem cells. There are two models for tumor progression: clonal (stochastic) and cancer stem cell (CSCs) models.