IntroductionLiver cancer is among the most prevalent malignant tumors worldwide. In 2020, an estimated 90.6 million individuals were diagnosed with the disease1,2, making it the third leading cause of cancer-related deaths globally, with a relative five-year survival rate of approximately 18%1. The high incidence and mortality underscore its poor prognosis. Liver cancer can be classified into primary liver cancer (PHC)—characterized by aggressive biological behavior—and secondary liver cancer3. Hepatocellular carcinoma (HCC, also referred to as LIHC) accounts for 80–90% of PHC cases, while intrahepatic cholangiocarcinoma contributes to 10–15%4. Most PHC patients have underlying chronic liver conditions, commonly due to hepatitis B virus (HBV) or hepatitis C virus (HCV) infection, with HBV predominating in Asia5.The slow pace of advancements in prevention and early detection has contributed to the rising incidence of PHC. Currently, the sensitivity of abdominal ultrasonography for early PHC detection remains suboptimal, especially in patients with obesity or non-viral liver disease6. While blood-based monitoring strategies show promise, they still require further validation before clinical practice6. In recent years, immune checkpoint inhibitor (ICI)-based therapies have become widely used for advanced PHC treatment7. Although ICIs, especially in combination with other agents, have improved survival outcomes for some patients, sustained benefits remain limited in late-stage disease. As a result, identifying novel molecular targets associated with liver cancer has become a major focus in PHC research and treatment.The human T-cell immunoglobulin and mucin domain-containing (TIM) gene family was first identified by McIntire’s team in 2001 during studies on asthma susceptibility8. This family includes three human TIM genes—TIM-1, TIM-3, and TIM-4—which encode proteins involved in immune regulation9,10. TIM-1 and TIM-3 are predominantly expressed on T cells and play roles in regulating apoptosis and immune tolerance11. In contrast, TIM-4, also known as TIMD4, (TIMD4 for Mesh name, TIM-4 for commonly used name) is mainly expressed on antigen-presenting cells such as macrophages and dendritic cells (DCs). As a natural ligand of TIM-1 and a phosphatidylserine receptor, TIM-4 facilitates the phagocytosis of apoptotic cells by macrophages. It also modulates T-cell activation and tolerance, contributing to immune homeostasis12. While the role of TIM-4 has been extensively investigated in malignancies such as cervical cancer, rectal cancer, and non-small cell lung cancer (NSCLC)13,14,15, its involvement in PHC remains poorly understood.Recent studies have shown that TIM-4 is highly expressed in monocytes from patients with acute ischemic stroke, particularly within non-classical and intermediate monocyte subsets16,17. Its expression was positively correlated with the National Institutes of Health Stroke Scale (NIHSS) score18. However, limited research exists on the regulatory function of TIM-4 in tumors, especially PHC. To date, no studies have reported the expression of TIM-4 in circulating monocytes or its concentration in plasma among PHC patients. In our bioinformatics analysis, we identified that TIM-4 exhibits reduced transcriptional and protein expression levels in liver cancer tissues. This pattern is distinct from the typical upregulation observed in many oncogenes and suggests that TIM-4 may have context-dependent biological functions. Notably, TIM-4 is highly expressed in other solid tumors, further highlighting its potential unique role in LIHC. Our analysis also revealed significant differential expression of TIMD4 in monocytes derived from LIHC patients. Monocytes and their related cell subsets play crucial regulatory roles in cancer development by influencing tumor growth, metastasis, and the tumor microenvironment19. Given the critical involvement of monocytes in these processes, we hypothesize that TIMD4 may be intricately linked to the function of monocytes during tumor progression in LIHC. This hypothesis forms the basis of our investigation and provides a strong rationale for focusing on the interplay between TIMD4 and monocytes in the context of LIHC.Therefore, this study investigates TIM-4 expression in peripheral blood monocytes and plasma of PHC patients, alongside established PHC screening markers such as alpha-fetoprotein (AFP) and routine liver function indicators—including alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), total bilirubin (TBIL), albumin (ALB), prealbumin (PA), and albumin-to-globulin ratio (A/G). The aim is to explore TIM-4’s diagnostic and therapeutic potential in PHC.Materials and methodsIdentification of TIM-4 (TIMD4) expression and clinical characteristics of PHC (LIHC)TIMD4 expression data and comprehensive clinical records were obtained from The Cancer Genome Atlas (TCGA) database (https://cancer.gov/tcga). Expression levels of TIMD4 across various cancer types and its correlation with clinical characteristics in liver hepatocellular carcinoma (LIHC) were analyzed using online bioinformatics platforms including TIMER2.0, GEPIA, HPA, and UALCAN. The expression of TIMD4 in tumor tissues was further validated through immunohistochemical data from the HPA database.Prognostic significance of TIMD4 in LIHCThe prognostic significance of TIMD4 in liver hepatocellular carcinoma was assessed using the Kaplan–Meier Plotter database. The TIMD4 gene identifier was selected, and the "automatic selection of the best cut-off" feature was employed.In our bioinformatics analysis, to determine the optimal cutoff value for defining “high” and “low” TIM-4 expression groups, we performed receiver operating characteristic (ROC) curve analysis. This analysis involves plotting the relationship between the true positive rate (sensitivity) and the false positive rate (1- specificity) at various threshold settings. By calculating the area under the ROC curve (AUC), we quantified the overall diagnostic accuracy of our test. The optimal cutoff value was determined by identifying the point on the ROC curve that corresponds to the maximum AUC, representing the best balance between sensitivity and specificity. This ensures accurate differentiation between the groups while minimizing misclassifications.Correlation genes with TIMD4 in LIHCUALCAN utilizes TCGA gene expression data from normal and tumor tissues to illustrate the expression patterns of specific genes in liver hepatocellular carcinoma (LIHC). Gene expression values are transformed into Log2(TPM + 1) values, with a color-coded scale where blue indicates low expression and red indicates high expression. A legend is provided to explain the color coding. Genes with very low expression (Median TPM