Although castration-resistant prostate cancer (CRPC) as a whole, by its name, refers to the tumors that relapse and/or regrow independently of androgen after androgen deprivation therapy (ADT), untreated tumor, even in early-stage primary prostate cancer (PCa), contains androgen-independent (AI) PCa cells. The transformation of androgen-dependent (AD) PCa to AI PCa under ADT is a forced evolutionary process, in which the small group of AI PCa cells that exist in primary tumors has the unique opportunity to proliferate and expand selectively and dominantly, while some AD PCa cells that have escaped from ADT-induced death acquire the capability to survive in an androgen-depleted environment. The adaptation and reprogramming of both PCa cells and the tumor microenvironment (TME) under ADT make PCa much stronger than primary tumors so that, currently, there are no effective therapeutic methods available for the treatment of CRPC. Many mechanisms have been found to be related to the emergence and maintenance of PCa castration resistance; in this review, we focus on the role of inflammatory signaling in both PCa cells and the TME for the emergence and maintenance of CRPC and summarize the recent advances of therapeutic strategies that target inflammatory signaling for the treatment of CRPC.

Purpose: Mannose receptor C-type 1 (MRC1) is an endocytic lectin receptor primarily expressed in macrophages, dendritic cells, and some endothelial cells. However, the role of MRC1 in cancers remains unclear. Methods: We analyzed MRC1 expression using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), and single-cell datasets. We systematically explored the prognostic implications and diagnostic value of MRC1. Immune-related indicators, including immune cells, immune scores, and immune checkpoint molecules, were used to estimate their correlation with MRC1 expression. Finally, we explored its potential ties to immunotherapy success markers such as tumor mutation burden and DNA repair genes. Results: MRC1 showed both pro- and anti-tumor leanings depending on the cancer types. High levels correlated with poorer outcomes in six cancers but improved prognosis in some cancers like glioblastoma multiforme. This trend extended to the immune arena, where MRC1 intertwined with diverse immune parameters, suggesting its influence on affecting the tumor’s immunological landscape. Intriguingly, its expression positively associated with factors favoring immunotherapy efficacy while negatively correlating with some potential barriers. Single-cell analysis pinpointed a specific link between MRC1 and DNA damage/repair pathways in breast cancer. Conclusion: Our study provides a comprehensive landscape of MRC1 levels and diverse regulatory patterns in different cancers, deepening the understanding of MRC1’s roles in tumorigenesis and immunity.

Renal ischemia–reperfusion injury (IRI) remains a critical obstacle to optimal renal transplant outcomes, driving acute graft dysfunction and long-term allograft failure. While ferroptosis—an iron-dependent form of cell death—has been linked to IRI pathogenesis, the role of lipocalin-2 (LCN2), a regulator of iron homeostasis and inflammation, in transplant-related renal IRI remains uncharacterized. Six murine IRI transcriptomic datasets (83 samples) were integrated using weighted gene co-expression network analysis (WGCNA) and differential expression profiling to screen for IRI-associated hub genes. Findings were validated in two human transplant cohorts (212 samples) via 113 machine learning algorithms, including logistic regression, random forest, and ensemble models. Single-cell RNA sequencing (GSE237429) was used to map gene expression to specific renal cell populations, while a murine warm IRI model evaluated the effects of LCN2 inhibition (ZINC00640089) on tubular injury, ferroptosis markers (MDA, GSH, Fe²⁺), and inflammatory cytokines (IL-6, TNF-α) across mild (50-minute) and severe (80-minute) ischemia subgroups. WGCNA identified 36 hub genes, with LCN2 emerging as a key node in ferroptosis and immune regulation pathways. A six-gene machine learning model, including LCN2, CLU, and SOX9, demonstrated robust predictive accuracy for IRI (AUC = 0.93). Single-cell analysis revealed elevated LCN2 expression in neutrophils and macrophages in IRI kidneys, correlated with increased immune cell infiltration. In vivo, LCN2 inhibition significantly reduced severe ischemia-induced tubular injury, suppressed lipid peroxidation (MDA), restored glutathione levels (GSH), and alleviated iron overload (Fe²⁺) and reactive oxygen species (ROS). Systemic inflammation was mitigated, with IL-6 and TNF-α levels significantly reduced. This study establishes LCN2 as a pivotal mediator of ferroptosis and immune dysregulation in transplant IRI. A machine learning-driven multi-omics approach provides a novel diagnostic framework, while the inhibition of LCN2 is shown to alleviate IRI-induced tissue damage in these models. These findings highlight the utility of integrative analytics in uncovering biological targets and offer new therapeutic avenues for improving kidney transplant outcomes.

To improve the survival of patients with hepatocellular carcinoma (HCC), new biomarkers and therapeutic targets are urgently needed. In this study, the GEO and TCGA dataset were used to explore the differential co-expressed genes and their prognostic correlation between HCC and normal samples. The mRNA levels of these genes were validated by qRT-PCR in 20 paired fresh HCC samples. The results demonstrated that the eight-gene model was effective in predicting the prognosis of HCC patients in the validation cohorts. Based on qRT-PCR results, NOX4 was selected to further explore biological functions within the model and 150 cases of paraffin-embedded HCC tissues were scored for NOX4 immunohistochemical staining. We found that the NOX4 expression was significantly upregulated in HCC and was associated with poor survival. In terms of function, the knockdown of NOX4 markedly inhibited the progression of HCC in vivo and in vitro. Mechanistic studies suggested that NOX4 promotes HCC progression through the activation of the epithelial–mesenchymal transition. In addition, the sensitivity of HCC cells to sorafenib treatment was obviously decreased after NOX4 overexpression. Taken together, this study reveals NOX4 as a potential therapeutic target for HCC and a biomarker for predicting the sorafenib treatment response.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by persistent mucosal inflammation. While fibroblasts contribute to tissue repair and fibrosis, their role in UC pathogenesis remains incompletely understood. Single-cell RNA sequencing (GSE114374, GSE231993) and cell-cell communication analysis were performed to profile fibroblast interactions. Weighted Gene Co-expression Network Analysis (WGCNA) identified fibroblast-related modules. Machine learning models predicted diagnostic biomarkers. IF, ELISA and WB assays were used to validate the expression of ANGPTL2 in UC samples. In vivo validation using a DSS-induced UC mouse model evaluated ANGPTL2 expression and therapeutic response to GDC-0152. Spatial transcriptomics (GSE189184) was used to analyze tissue regionalization, cell-type spatial distribution, and the localized expression of ANGPTL2 and its colocalization with fibroblasts. Single-cell analysis identified fibroblasts as a key cell type in UC. WGCNA revealed a strong correlation between the turquoise module and fibroblast infiltration, with genes in this module enriched in pathways such as cytokine-cytokine receptor interaction and TNF signaling. Machine learning models identified PHLDA1 and ANGPTL2 as potential biomarkers with high diagnostic accuracy (AUC 0.935 and 0.917, respectively). Furthermore, exprimental analyses confirmed that ANGPTL2 was significantly upregulated in fibroblasts from UC patients, serum ELISA experiments demonstrated that ANGPTL2 was also significantly elevated in the serum of UC patients, supporting its potential as a non-invasive diagnostic indicator. In vivo experiments confirmed ANGPTL2 upregulation in DSS-treated mice, reversed by GDC-0152. Spatial transcriptomics partitioned UC tissue into 13 distinct regions, revealing prominent fibroblast distribution and markedly high ANGPTL2 expression with significant spatial colocalization with fibroblasts. Fibroblasts are central to UC pathogenesis, contributing to inflammation and fibrosis. ANGPTL2 is a promising diagnostic biomarker and potential therapeutic target in UC.