Tumor-associated macrophages (TAMs), derived from circulating monocytes recruited to tumor sites via chemotactic signals such as C-C motif ligand 2 (CCL2) and colony-stimulating factor-1 (CSF-1), are pivotal components of the tumor microenvironment (TME). Functionally polarized into distinct subtypes, TAMs play dual roles: proinflammatory M1-type TAMs enhance antitumor immunity through the secretion of cytokines such as interleukin-12 (IL-12) and tumor necrosis factor alpha (TNF-α) and direct tumor cell cytotoxicity, whereas M2-type TAMs promote tumor progression by facilitating angiogenesis, metastasis, and immunosuppression. This polarization is dynamically regulated by different cytokines, various signaling pathways, and metabolic cues within the TME. Spatial distribution analyses revealed that M2-like TAMs predominantly infiltrate hypoxic and stromal regions, where they secrete factors such as vascular endothelial growth factor (VEGF), transforming growth factor beta (TGF-β), and matrix metalloproteinases (MMPs) to remodel the extracellular matrix and suppress immune responses via programmed death-ligand 1 (PD-L1) and arginase-1 upregulation. Crucially, TAMs interact extensively with immune cells; M2-TAMs secrete interleukin-10 (IL-10) and TGF-β to inhibit cytotoxic T lymphocytes while expanding regulatory T (Treg) cells and impairing natural killer (NK) cell function via altered antigen presentation. Conversely, M1-TAMs synergize with dendritic cells to enhance T-cell priming. Therapeutically, targeting TAMs offers promising strategies, including colony-stimulating factor-1 receptor (CSF-1R) inhibitors, CCL2 antagonists, and nanoparticle-mediated repolarization of M2-TAMs toward the M1 phenotype. Emerging genetic approaches, such as clustered regularly interspaced short palindromic repeat-CRISPR-associated protein 9 (CRISPR-Cas9) editing, aim to disrupt protumorigenic pathways in TAMs. Additionally, TAM-related biomarkers (e.g., CD206 and CD163) are being evaluated for their prognostic and predictive utility in immunotherapies. Despite progress, challenges persist owing to TAM plasticity and TME heterogeneity across cancers. This review synthesizes TAM biology, immune crosstalk, and therapeutic advancements, providing a foundation for novel oncology strategies aimed at reprogramming TAMs to overcome treatment resistance and improve clinical outcomes.

Background: The proteome is a major source of therapeutic targets. We conducted a proteome-wide Mendelian randomization (MR) study to identify candidate protein markers and therapeutic targets for colorectal cancer (CRC). Methods: Protein quantitative trait loci (pQTLs) were derived from seven published genome-wide association studies (GWASs) on plasma proteome, and summary-level data were extracted for 4853 circulating protein markers. Genetic associations with CRC were obtained from a large-scale GWAS meta-analysis (16,871 cases and 26,328 controls), the FinnGen cohort (4957 cases and 304,197 controls), and the UK Biobank (9276 cases and 477,069 controls). Colocalization and summary-data-based MR (SMR) analyses were performed sequentially to verify the causal role of candidate proteins. Single cell-type expression analysis, protein-protein interaction (PPI), and druggability evaluation were further conducted to detect the specific cell type with enrichment expression and prioritize potential therapeutic targets. Results: Collectively, genetically predicted levels of 13 proteins were associated with CRC risk. Elevated levels of two proteins (GREM1, CHRDL2) and decreased levels of 11 proteins were associated with an increased risk of CRC, among which four (GREM1, CLSTN3, CSF2RA, CD86) were prioritized with the most convincing evidence. These protein-coding genes are mainly expressed in tissue stem cells, epithelial cells, and monocytes in colon tumor tissue. Two interactive pairs of proteins (GREM1 and CHRDL2; MMP2 and TIMP2) were identified to be involved in osteoclast differentiation and tumorigenesis pathways; four proteins (POLR2F, CSF2RA, CD86, MMP2) have been targeted for drug development on autoimmune diseases and other cancers, with the potentials of being repurposed as therapeutic targets for CRC. Conclusions: This study identified several protein biomarkers to be associated with CRC risk and provided new insights into the etiology and promising targets for the development of screening biomarkers and therapeutic drugs for CRC.

This study aims to identify colorectal cancer (CRC)-related proteomic profiles and develop a prediction model for CRC onset by integrating proteomic profiles with genetic and non-genetic factors (QCancer-15) to improve the risk stratification and estimate of personalized initial screening age. Here, using a two-stage strategy, we prioritize 15 protein biomarkers as predictors to construct a protein risk score (ProS). The risk prediction model integrating proteomic profiles with polygenic risk score (PRS) and QCancer-15 risk score (QCancer-S) shows improved performance (C-statistic: 0.79 vs. 0.71, P = 4.94E–03 in training cohort; 0.75 vs 0.69, P = 5.49E–04 in validation cohort) and net benefit than QCancer-S alone. The combined model markedly stratifies the risk of CRC onset. Participants with high ProS, PRS, or combined risk score are proposed to start screening at age 46, 41, or before 40 years old. In this work, the integration of blood proteomics with PRS and QCancer-15 demonstrates improved performance for risk stratification and clinical implication for the derivation of risk-adapted starting ages of CRC screening, which may contribute to the decision-making process for CRC screening.

Purpose. Colorectal cancer is one of the most common malignant primary tumors, prone to metastasis, and associated with a poor prognosis. As autophagy is closely related to the development and treatment of colorectal cancer, we investigated the potential prognostic value of long noncoding RNA (lncRNA) associated with autophagy in colorectal cancer. Methods. In this study, we acquired information on the expression of lncRNAs in colorectal cancer from the Cancer Genome Atlas (TCGA) database and found that 860 lncRNAs were associated with autophagy-related genes. Subsequently, univariate Cox regression analysis was used to investigate 32 autophagy-related lncRNAs linked to colon cancer prognosis. Subsequently, eight of the 32 autophagy-related lncRNAs (i.e., long intergenic nonprotein coding RNA 1503 [LINC01503], ZEB1 antisense RNA 1 [ZEB1-AS1], AC087481.3, AC008760.1, AC073896.3, AL138756.1, AL022323.1, and TNFRSF10A-AS1) were selected through multivariate Cox regression analysis. Based on these autophagy-related lncRNAs, a risk signature was constructed, and the patients were divided into high- and low-risk groups. Results. The high-risk group’s overall survival time was significantly shorter than that of the low-risk group . Receiver operating characteristic curve analysis was performed to further confirm the validity of the model (area under the curve: 0.689). Moreover, multivariate regression suggested that the risk score was a significant prognostic risk factor in colorectal cancer. Gene set enrichment analysis showed that these gene sets are significantly enriched in cancer-related pathways, such as Kirsten rat sarcoma viral oncogene homolog (KRAS) signaling. Conclusion. The risk signature of eight autophagy-related lncRNAs has prognostic potential for colorectal cancer. These autophagy-related lncRNAs may play a vital role in the biology of colorectal cancer.