Metastasis is the leading cause of high mortality in colorectal cancer (CRC), which is not only driven by changes occurring within the tumor cells, but is also influenced by the dynamic interaction between cancer cells and components in the tumor microenvironment (TME). Currently, the exploration of TME remodeling and its impact on CRC metastasis has attracted increasing attention owing to its potential to uncover novel therapeutic avenues. Noteworthy, emerging studies suggested that tumor-associated macrophages (TAMs) within the TME played important roles in CRC metastasis by secreting a variety of cytokines, chemokines, growth factors and proteases. Moreover, TAMs are often associated with poor prognosis and drug resistance, making them promising targets for CRC therapy. Given the prognostic and clinical value of TAMs, this review provides an updated overview on the origin, polarization and function of TAMs, and discusses the mechanisms by which TAMs promote the metastatic cascade of CRC. Potential TAM-targeting techniques for personalized theranostics of metastatic CRC are emphasized. Finally, future perspectives and challenges for translational applications of TAMs in CRC development and metastasis are proposed to help develop novel TAM-based strategies for CRC precision medicine and holistic healthcare.

Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.

Parkinson’s disease (PD) is a common neurological disease in elderly people, and its morbidity and mortality are increasing with the advent of global ageing. The traditional paradigm of moving from small data to big data in biomedical research is shifting toward big data-based identification of small actionable alterations. To highlight the use of big data for precision PD medicine, we review PD big data and informatics for the translation of basic PD research to clinical applications. We emphasize some key findings in clinically actionable changes, such as susceptibility genetic variations for PD risk population screening, biomarkers for the diagnosis and stratification of PD patients, risk factors for PD, and lifestyles for the prevention of PD. The challenges associated with the collection, storage, and modelling of diverse big data for PD precision medicine and healthcare are also summarized. Future perspectives on systems modelling and intelligent medicine for PD monitoring, diagnosis, treatment, and healthcare are discussed in the end.

Background: Epithelial-mesenchymal transition (EMT) has been associated with the angiogenesis and oncogenic phenotypes of multiple malignant tumors including bladder cancer (BCa). Circular RNAs (circRNAs) are recognized as crucial regulators in the EMT. This study aims to illustrate the possible role of circular RNA_0000658 (circ_0000658) in BCa and the underlying molecular mechanism. Methods: The expression of circ_0000658, microRNA (miR)-498, and high mobility group AT-hook 2 (HMGA2) was assessed in cancer and adjacent normal tissue collected from BCa patients and human BCa cell lines (MGH-U3, T24, 5637 and SW780). BCa cells were transduced with a series of overexpression or shRNA plasmids to clarify the function of circ_0000658 and miR-498 on the oncogenic phenotypes and EMT of BCa cells. Further, we established nude mice xenografted with BCa cells to validate the roles of circ_0000658 on tumor growth in vivo. Results: Circ_0000658 was highly expressed in BCa tissue samples and cell lines, which indicated a poor prognosis of BCa patients. Circ_0000658 competitively bound to miR-498 and thus restricted miR-498 expression. Meanwhile, circ_0000658 weakened the binding of miR-498 to the target gene HMGA2 and upregulated the HMGA2 expression. Circ_0000658 elevation or miR-498 knockdown augmented oncogenic phenotypes and EMT of BCa cells, corresponding to a reduction in the expression of β-catenin and E-cadherin as well as an increase in the expression of N-cadherin, Slug, Snail, ZEB1 and Twist. Inhibition of HMGA2 reversed the effects of circ_0000658 overexpression on tumor growth in vivo. Conclusion: Altogether, our study uncovered the tumor-promoting role of circ_0000658 in BCa via the miR-498/HMGA2 axis.

Colorectal cancer (CRC) continues to rise in global incidence and remains a leading cause of cancer-related mortality. Immunogenic cell death (ICD) has emerged as a critical modulator of tumor microenvironment (TME) dynamics; however, its prognostic implications and therapeutic potential in CRC require systematic characterization. Through the integrative analysis of single-cell RNA sequencing and bulk transcriptomic data, 11 ICD-related genes with prognostic significance were identified in CRC. A comprehensive computational framework was then employed to evaluate 101 machine learning combinations, ultimately constructing an optimized 11-gene ICD-related signature (ICDRS) by integrating StepCox [forward] and RSF. The ICDRS exhibited strong predictive performance for overall survival in CRC patients across the training and validation datasets. Notably, the ICDRS-based nomogram achieved outstanding time-dependent AUCs (>0.90) for 1- to 3-year survival prediction. Multidimensional analysis revealed significant associations between ICDRS-derived risk score and distinct immune infiltration patterns, immunotherapy response and TME characteristics. Furthermore, a novel macrophage subtype, SPP1⁺/SLC11A1⁺, was discovered and characterized by high infiltration levels. Drug repurposing analysis indicated Olaparib as a potential therapeutic candidate for high-risk CRC patients. Therefore, this study establishes ICDRS as a promising tool for CRC prognosis and immunotherapy, with future validation studies planned to guide personalized treatment strategies.