Abstract Taxanes play a crucial role in cancer treatment, particularly for non-small cell lung cancer and breast cancer. However, real-world studies examining drug-induced liver injury (DILI) associated with these drugs remain limited. Our study investigates the association between taxanes and DILI through analysis of the Food and Drug Administration Adverse Event Reporting System (FAERS) database, alongside an exploration of potential hepatotoxicity mechanisms via network pharmacology. We collected DILI reports related to taxanes from the FAERS database between January 2004 and March 2024, employing disproportionality analyses with the reporting odds ratio (ROR) and 95% confidence intervals. Our findings revealed a significant association between paclitaxel (ROR = 2.35) and nab-paclitaxel (ROR = 3.14) with DILI, while docetaxel demonstrated no significant correlation (ROR = 0.68), although it was linked to higher mortality rates and earlier onset. Network pharmacology analysis uncovered that the mechanisms of liver injury induced by these two drugs may not be entirely congruent. Unique targets for docetaxel included BCL2, CNR2, and MAPK1, while the ‘Regulation of lipolysis in adipocytes’ pathway was specifically associated with docetaxel-induced DILI. Our findings indicate that taxanes exhibit differential hepatotoxic risks and hepatotoxicity mechanisms, emphasizing the need for enhanced drug safety monitoring strategies for cancer patients. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-99669-3. Keywords: Taxanes, Drug-Induced liver injury, FAERS database, Disproportionality analyses, Reporting odds ratio, Network Pharmacology Subject terms: Drug safety, Data mining Introduction The use of taxanes in cancer treatment has been gradually increasing, particularly in non-small cell lung cancer and breast cancer^[34]1. Taxanes, which include paclitaxel, docetaxel, and cabazitaxel, as well as various formulations such as nab-paclitaxel and paclitaxel liposomes, have become integral parts of contemporary oncological treatment regimens^[35]2,[36]3. Recent studies have reported that the administration of taxanes is often associated with drug-induced liver injury (DILI) during cancer therapy^[37]4. However, the specific clinical manifestations and underlying mechanisms of DILI related to taxanes in cancer patients have not been well documented. DILI shows various clinical manifestations, varying from the increasement of hepatic transaminases to hepatic failure, and even result in patient mortality^[38]5. It is necessary to comprehensively understand the safety and associated risks of taxanes for the optimization of cancer treatment. Current research on the adverse events of taxanes primarily derives from short-term clinical trials, which often do not capture rare but serious adverse events like DILI. There is also limited literature reporting on the real-world implications of taxanes associated liver injury. The Food and Drug Administration Adverse Event Reporting System (FAERS) provides a valuable opportunity to explore these associations on a broader scale, enabling researchers to examine post-market drug safety data more thoroughly^[39]6. Investigating these associations through large-scale data analysis could bridge existing gaps in our understanding of taxane-induced liver injury. Although previous studies have indicated that taxanes may influence liver metabolism^[40]7,[41]8, systematic analyses focusing on the interactions between taxanes and genes associated with liver injury remain scarce. Network pharmacology, an emerging bioinformatics analysis method developed in recent years, can be used to systematically and comprehensively predict the mechanisms of drug action^[42]9,[43]10. In this study, we utilized the FAERS database to identify DILI events associated with the use of taxanes, thereby elucidating the relationship between taxanes and liver injury. Based on this, network pharmacology techniques were employed to explore the potential mechanisms of DILI associated with taxanes, preliminarily revealing the molecular targets and the similarities and differences in their mechanisms of inducing DILI. This research can provide valuable insights into the mechanisms of DILI associated with taxanes, contributing to the improvement of medication safety for cancer patients. Results Descriptive analysis From the first quarter of 2004 to the first quarter of 2024, a total of 50,390,907 adverse event reports (AERs) were collected from the FAERS database. Of these, there were 156,120 records of adverse events (AEs) for the three target drugs classified as primary suspected (PS) drugs. The most of these reports originated from North America and certain European countries, with the specific distribution areas illustrated in Fig. [44]1A-C. Among these reports, there were 2,168 AERs related to DILI, including 1,196 reports (55.17%) for paclitaxel, 238 reports (10.98%) for docetaxel, and 734 reports (33.86%) for nab-paclitaxel. The annual distribution of these reports is depicted in Fig. [45]1D. Additionally, we can see that the number of female patients is greater than that of male patients from Table [46]1. Regarding age distribution, most patients are concentrated between 18 and 65 years old, but elderly individuals over 65 also account for nearly one-third, which requires attention. Most reporters are healthcare professionals, with reports from consumers being relatively low, all below 10%. The geographical distribution of reports reveals that liver injury associated with paclitaxel is most frequently documented in European countries such as France and Italy. In contrast, reports for docetaxel often lack specific origin information, while nab-paclitaxel reports are mainly from Japan and the United States. China shows a reporting rate of less than 10% despite its extensive population, which may be attributed to its national adverse reaction reporting system and the utilization of locally marketed paclitaxel formulations. The median time to onset of liver injury is 21 days for both paclitaxel and nab-paclitaxel, whereas docetaxel tends to cause liver injury earlier, usually within 11 days. Fig. 1. [47]Fig. 1 [48]Open in a new tab Geographical distribution of adverse events to taxanes and annual changes in the incidence of liver injury. (A–C) The geographical distribution maps of three taxanes. (D) The number of three taxanes inducing liver injury from the first quarter of 2004 to the first quarter of 2024 report. Table 1. Characteristics of reports on taxanes-associated dilis in the FAERS database (January 2004 to March 2024). Paclitaxel Docetaxel Nab-paclitaxel Gender Female (%) 864 (72.24) 143 (60.08) 352 (47.96) Male (%) 219 (18.31) 84 (35.29) 251 (34.20) Unknown (%) 113 (9.45) 11 (4.62) 131 (17.85) Age < 18 (%) 6 (0.50) – – 18–65 (%) 643 (53.76) 122 (51.26) 294 (40.05) ≥ 65 (%) 328 (27.42) 74 (31.09) 247 (33.65) Unknown (%) 219 (18.31) 42 (17.65) 193 (26.29) Reporter Physician (%) 480 (40.13) 94 (39.50) 285 (38.83) Pharmacist (%) 289 (24.16) 27 (11.34) 90 (12.26) Other health-professional (%) 283 (23.66) 69 (28.99) 304 (41.42) Consumer (%) 104 (8.70) 19 (7.98) 34 (4.63) Lawyer (%) – – 1 (0.14) Unknown (%) 40 (3.34) 29 (12.18) 20 (2.72) Reported countries Germany 36 (3.01) – 73 (9.95) Canada 22 (1.84) 15 (6.30) 58 (7.90) Australia – – 12 (1.50) Poland 20 (1.67) – – Finland – – 11 (1.50) Austria 14 (1.17) – – Egypt 11 (0.92) – – Belgium 10 (0.84) – – Portugal 9 (0.75) – – Slovenia 9 (0.75) – – France 201 (16.81) 14 (5.88) 26 (3.54) Italy 175 (14.63) – 52 (7.08) USA 95 (7.94) 24 (10.08) 110 (14.99) Japan 91 (7.61) 14 (5.88) 123 (16.76) China 71 (5.94) – 61 (8.31) Netherlands 70 (5.85) – – Spain 65 (5.43) – 49 (6.68) UK 39 (3.26) – 16 (2.18) Other 258 (21.57) 171 (71.85) 143 (19.48) Time-to-onset (days) Median (q1, q3) 21.00 (7.00, 58.75) 11.00 (5.00, 29.00) 21.00 (7.00, 71.00) [49]Open in a new tab Liver injury is a critical condition that can result in severe adverse consequences. In our study, we compiled and analyzed the outcomes of adverse events related to liver injury, as illustrated in Fig. [50]2. Our findings indicate that while docetaxel is associated with the lowest incidence of liver injury among the drugs studied, it showed a statistically higher mortality rate compared to paclitaxel (χ² = 19.2, p < 0.0001). Specifically, the mortality rate associated with liver injury was 25.21% for docetaxel, compared to 14.34% for paclitaxel and 18.01% for nab-paclitaxel. These observations suggest that liver injury outcomes may vary across taxanes, with docetaxel-associated cases warranting particular attention in clinical monitoring. Fig. 2. [51]Fig. 2 [52]Open in a new tab Outcome of liver injury caused by three taxanes. Signal detection of DILI-related AEs in the FAERS database Signal detection of the results revealed an association between taxanes and DILI. Paclitaxel (ROR = 2.35) and nab-paclitaxel (ROR = 3.14) were associated with an increased incidence of DILI, while docetaxel (ROR = 0.68) had no significant impact on the incidence of DILI. The specific signal detection results for adverse events under certain Preferred Terminology (PT) conditions are shown in Table [53]2. Paclitaxel had positive signals in 14 PT terms, including 30 patients with pseudocirrhosis (ROR = 61.77), 41 patients with hepatic atrophy (ROR = 49.81), 99 patients with hypertransaminasaemia (ROR = 10.51), and immune-mediated hepatitis (ROR = 6.81), among others. Docetaxel had only 9 positive signals, mainly related to liver tenderness (ROR = 26.33), decreased bilirubin (ROR = 11.86), bilirubin conjugated increased (ROR = 3.38) and so on. Nap-paclitaxel, like paclitaxel, also showed 14 positive signals, including a similar pseudocirrhosis (ROR = 18.81), immune-mediated hepatitis (ROR = 13.07), and hypertransaminasaemia (ROR = 3.52), but nab-paclitaxel also has some abnormalities related to bilirubin, such as blood bilirubin abnormality (ROR = 4.60) and cholestatic jaundice (ROR = 2.60). Table 2. Positive signal strength for liver injuries associated with taxanes based on PT levels of FAERS. soc pt Paclitaxel Docetaxel Nab-Paclitaxel N ROR (95% CI) IC (IC025) N ROR (95% CI) IC (IC025) N ROR (95% CI) IC (IC025) Hepatobiliary disorders Pseudocirrhosis 30 61.77 (42.04, 90.77) 5.74 (5.2) – – – 7 18.81 (8.85, 39.98) 4.18 (3.16) Hepatic atrophy 41 49.81 (35.94, 69.04) 5.45 (4.99) – – – – – – Hypertransaminasaemia 99 10.51 (8.56, 12.91) 3.28 (2.99) – – – 24 3.52 (2.35, 5.29) 1.79 (1.21) Immune-mediated hepatitis 17 6.81 (4.21, 11.02) 2.74 (2.06) – – – 22 13.07 (8.53, 20.03) 3.65 (3.05) Hepatic cytolysis 60 4.36 (3.37, 5.65) 2.07 (1.7) – – – – – – Mixed liver injury 15 3.4 (2.04, 5.67) 1.75 (1.04) – – – – – – Hepatitis acute 41 3.3 (2.42, 4.5) 1.69 (1.25) – – – 4 0.51 (0.19, 1.36) − 0.97 (− 2.23) Hepatitis fulminant 15 2.53 (1.52, 4.22) 1.33 (0.61) 5 3.38 (1.4, 8.18) 1.74(0.58) 3 0.86 (0.28, 2.68) − 0.21 (− 1.63) Hepatocellular injury 68 2.36 (1.85, 3.01) 1.2 (0.85) 13 1.75 (1.01, 3.05) 0.79 (0.02) 4 0.2 (0.07, 0.52) − 2.32 (− 3.59) Hyperbilirubinaemia 41 2.11 (1.55, 2.89) 1.06 (0.61) 7 1.46 (0.69, 3.08) 0.53 (− 0.48) 19 1.52 (0.96, 2.39) 0.59 (− 0.05) Jaundice cholestatic 11 1.55 (0.86, 2.81) 0.63 (− 0.19) – – – 11 2.6 (1.43, 4.72) 1.37 (0.54) Cholestasis 56 1.55 (1.19, 2.03) 0.62 (0.23) 5 0.54 (0.23, 1.32) − 0.86 (− 2.02) 14 0.61 (0.36, 1.03) − 0.7 (− 1.44) Hepatotoxicity 63 1.52 (1.18, 1.96) 0.59 (0.22) 3 0.3 (0.09, 0.92) − 1.73 (− 3.15) 36 1.35 (0.97, 1.88) 0.42 (− 0.06) Hepatic failure 87 1.47 (1.18, 1.83) 0.53 (0.22) 32 2.19 (1.52, 3.14) 1.06 (0.55) 64 1.82 (1.41, 2.35) 0.82 (0.46) Hepatic function abnormal 85 1.23 (0.98, 1.53) 0.28 (− 0.03) 20 1.18 (0.76, 1.86) 0.23 (− 0.4) 80 1.89 (1.51, 2.38) 0.86 (0.54) Hepatic lesion 9 1.09 (0.57, 2.1) 0.12 (− 0.77) – – – 10 1.89 (1.01, 3.52) 0.91 (0.05) Hepatic pain 8 0.96 (0.48, 1.92) − 0.06 (− 1.01) 4 1.99 (0.74, 5.33) 0.98 (− 0.29) 3 0.55 (0.18, 1.7) − 0.86 (− 2.28) Liver injury 35 0.89 (0.64, 1.25) − 0.16 (− 0.63) 4 0.46 (0.17, 1.22) − 1.11 (− 2.39) 10 0.38 (0.2, 0.7) − 1.38 (− 2.24) Drug-induced liver injury 44 0.87 (0.64, 1.17) − 0.2 (− 0.63) 4 0.35 (0.13, 0.93) − 1.5 (− 2.78) 19 0.54 (0.34, 0.85) − 0.87 (− 1.51) Hepatitis 40 0.8 (0.59, 1.1) − 0.31 (− 0.75) – – – 24 0.8 (0.53, 1.19) − 0.32 (− 0.89) Acute hepatic failure 18 0.7 (0.44, 1.12) − 0.5 (− 1.15) – – – 3 0.17 (0.06, 0.54) − 2.49 (− 3.91) Jaundice 39 0.7 (0.51, 0.96) − 0.5 (− 0.95) 19 1.36 (0.86, 2.15) 0.42 (− 0.23) 47 1.43 (1.07, 1.92) 0.5 (0.08) Hepatomegaly 13 0.63 (0.37, 1.09) − 0.65 (− 1.41) 9 1.76 (0.91, 3.4) 0.8 (− 0.11) 8 0.67 (0.33, 1.33) − 0.58 (− 1.53) Autoimmune hepatitis 8 0.61 (0.31, 1.23) − 0.7 (− 1.65) – – – 14 1.69 (1, 2.86) 0.75 (0.01) Hepatitis cholestatic 7 0.61 (0.29, 1.28) − 0.71 (− 1.71) – – – – – – Hepatic cirrhosis 21 0.6 (0.39, 0.92) − 0.73 (− 1.33) 10 1.19 (0.63, 2.22) 0.24 (− 0.62) 9 0.4 (0.21, 0.77) − 1.3 (− 2.2) Hepatitis toxic 3 0.56 (0.18, 1.74) − 0.83 (− 2.25) – – – – – – Hepatic necrosis 4 0.5 (0.19, 1.34) − 0.98 (− 2.25) 4 1.94 (0.72, 5.2) 0.95 (− 0.33) – – – Hepatic steatosis 17 0.46 (0.29, 0.75) − 1.09 (− 1.76) 4 0.44 (0.17, 1.19) − 1.15 (− 2.42) 8 0.36 (0.18, 0.71) − 1.47 (− 2.42) Liver disorder 25 0.28 (0.19, 0.42) − 1.76 (− 2.32) 15 0.72 (0.43, 1.2) − 0.46 (− 1.18) 50 0.93 (0.7, 1.24) − 0.1 (− 0.5) Liver tenderness – – – 4 26.33 (9.75, 71.1) 4.68 (3.4) – – – Hepatobiliary disease – – – – – – 5 6.67 (2.76, 16.14) 2.72 (1.55) Ocular icterus – – – – – – 3 0.55 (0.18, 1.72) − 0.85 (− 2.26) Investigations Transaminases increased 81 1.9 (1.52, 2.38) 0.89 (0.57) 6 0.54 (0.24, 1.21) − 0.87 (− 1.95) 57 2.1 (1.61, 2.75) 1.03 (0.64) Aspartate aminotransferase increased 123 1.16 (0.97, 1.4) 0.2 (− 0.06) 45 1.75 (1.28, 2.39) 0.73 (0.29) 106 1.84 (1.5, 2.25) 0.81 (0.52) Gamma-glutamyltransferase increased 51 1.12 (0.84, 1.47) 0.15 (− 0.25) 10 0.84 (0.45, 1.57) − 0.24 (− 0.11) 27 1.03 (0.7, 1.51) 0.04 (− 0.5) Alanine aminotransferase increased 136 1.11 (0.93, 1.33) 0.14 (− 0.11) 53 1.81 (1.36, 2.42) 0.77 (0.36) 113 1.66 (1.36, 2.02) 0.66 (0.38) Alanine aminotransferase abnormal 4 1.05 (0.39, 2.79) 0.06 (− 1.2) – – – – – – Blood bilirubin increased 37 0.67 (0.48, 0.93) − 0.56 (− 1.03) 37 2.83 (2.02, 3.98) 1.41 (0.93) 77 2.5 (1.98, 3.16) 1.25 (0.92) Liver function test abnormal 30 0.48 (0.33, 0.69) − 1.03 (− 1.54) 8 0.5 (0.25, 1) − 0.91 (− 1.93) 28 0.76 (0.52, 1.11) − 0.38 (− 0.91) Liver function test increased 17 0.45 (0.28, 0.73) − 1.12 (− 1.79) 3 0.34 (0.11, 1.07) − 1.52 (− 2.94) 17 0.67 (0.41, 1.08) − 0.57 (− 1.25) Hepatic enzyme increased 53 0.4 (0.3, 0.52) − 1.26 (− 1.65) 13 0.41 (0.24, 0.72) − 1.21 (− 1.98) 18 0.21 (0.13, 0.33) − 2.18 (− 2.83) Blood bilirubin decreased – – – 3 11.86 (3.79, 37.08) 3.55 (2.12) – – – Bilirubin conjugated increased – – – 5 4.23 (1.75, 10.23) 2.06 (0.9) – – – Hepatic enzyme abnormal – – – 4 1.76 (0.66, 4.7) 0.8 (− 0.47) 4 0.64 (0.24, 1.7) 0.65 (− 1.92) Ammonia increased – – – 3 1.13 (0.36, 3.53) 0.18 (− 1.24) – – – Blood bilirubin abnormal – – – – – – 7 4.6 (2.18, 9.69) 2.18 (1.18) Aspartate aminotransferase abnormal – – – – – – 3 1.81 (0.58, 5.64) 0.86 (− 0.56) Neoplasms benign, malignant and unspecified (incl cysts and polyps) Hepatic cancer metastatic 3 1.71 (0.55, 5.32) 0.77 (− 0.65) – – – – – – Hepatic neoplasm – – – – – – 5 1.2 (0.5, 2.89) 0.26 (− 0.9) Blood and lymphatic system disorders Acquired haemophilia 3 1.66 (0.53, 5.17) 0.73 (− 0.69) – – – – – – [54]Open in a new tab Drug-liver injury-related gene interaction network analysis A total of 116 potential targets for docetaxel and 122 potential targets for paclitaxel were predicted, linked to 1715 genes associated with liver injury (Supplementary Table [55]S1-S3). As shown in Fig. [56]3, by intersecting these gene sets, 48 interactive target genes were identified that represent the overlap between the targets of docetaxel and the genes implicated in hepatic injury. Similarly, 53 interactive target genes were identified representing the overlap between the targets of paclitaxel and the liver injury-related genes. Hypergeometric testing demonstrated significant enrichment of overlapping targets between taxanes and liver injury genes. For docetaxel, 48 of its 116 predicted targets overlapped with liver injury genes (expected by chance = 10.27; P = 1.16 × 10⁻^21; enrichment ratio = 4.67). Similarly, paclitaxel showed 53 overlaps among 122 targets (expected = 10.79; P = 5.62 × 10^-2^6; enrichment ratio = 4.91), indicating strong non-random associations between taxane targets and hepatic injury mechanisms. Comparative analysis of the potential targets for liver injury induced by both drugs revealed that while paclitaxel and docetaxel share some common targets, there are also distinct targets unique to each. Specifically, there are 35 shared targets, with 13 exclusive targets for docetaxel and 18 exclusive targets for paclitaxel, as shown in Table [57]3. To elucidate the interrelationships among the potential targets, the Protein-Protein Interaction (PPI) was performed by using the STRING database ([58]https://string-db.org). This facilitated the construction of a protein interaction network using Cytoscape 3.7.2 software, with the results showed in Fig. [59]4. For docetaxel, key targets (central nodes of the interaction) included EGFR, BCL2, HSP90AA1, MMP9, JAK2, HSP90AB1, IGF1R, KDR, MMP2, and MAPK1. For paclitaxel, key targets (central nodes of the interaction) included TNF, STAT3, EGFR, ERBB2, MMP9, HSP90AA1, MCL1, MMP2, IGF1R, and HSP90AB1. Topological analysis revealed their centrality within the network. Fig. 3. [60]Fig. 3 [61]Open in a new tab Potential targets of drug-induced liver injury. (A) Paclitaxel; (B) Docetaxe. The overlaps meant the potential targets. Table 3. Common and differential targets associated with docetaxel- and paclitaxel-induced liver injury. Common targets Paclitaxel-specific targets Docetaxel-specific targets EGFR, TACR2, CYP3A4, CCKAR, ABCB1, MERTK, EDNRA, CDK1, SIRT2, CDK2, MET, ALK, ROS1, IGF1R, KDR, PIK3CG, PDE5A, PTPA, MAPK14, PIK3CB, KLK3, CNR1, AURKA, CDK4, MAPK8, EPHB4, JAK2, PIK3CD, JAK1, HSP90AA1, PTPN1, ADORA2A, MMP9, MMP2, HSP90AB1 ERBB2, MMP8, PRKCA, CCNE1, HDAC6, MCL1, MMP1, PSMB9, CTSB, ADAM17, PSMB8, TNF, SCARB1, ITGAL, STAT3, CXCR2, NR1I2, ABCB11 MAPK1, INSR, BACE1, SLC29A1, BCL2, F2, F10, ALOX5, CNR2, CHRM2, CCR1, CCR5, NOD2 [62]Open in a new tab Fig. 4. [63]Fig. 4 [64]Open in a new tab PPI network of targets associated with drug-induced liver injury. (A) Paclitaxel; (B) Docetaxe. The nodes with higher degree owned dark color. To further understand the involvement of docetaxel- and paclitaxel-induced liver injury target genes in biological signaling pathways, KEGG pathway enrichment analysis was conducted. The top 20 pathways for comprehensive mapping were illustrated in Fig. [65]5. Comparative analysis of the KEGG pathways revealed 132 shared pathways, with 5 specific pathways for docetaxel, namely ‘Ovarian Steroidogenesis’, ‘Regulation of Lipolysis in Adipocytes’, ‘B Cell Receptor Signaling Pathway’, ‘Parathyroid Hormone Synthesis, Secretion and Action’, and ‘Alcoholism’ (Supplementary Table [66]S4). The above findings indicate that while docetaxel is a derivative of paclitaxel, the targets and mechanisms involved in the induced liver injury are similar but not entirely identical. Fig. 5. [67]Fig. 5 [68]Open in a new tab KEGG pathway enrichment analysis. (A) Paclitaxel; (B) Docetaxel. Each bubble represents a specific pathway. The horizontal axis represents the extent of enrichment for each pathway, while the size of the bubbles indicates the number of genes enriched in the corresponding pathway. Color indicates significance, with a gradient from green to red representing decreasing q-values. Discussion With the widespread use of taxanes in cancer treatment, achieving a balance between risks and benefits has become particularly crucial. Drug-induced liver injury, as a rare but serious adverse reaction of taxanes, poses a significant risk to patient safety and treatment efficacy. The underlying mechanisms of DILI remain inadequately understood, potentially involving direct hepatocyte damage, immune-mediated responses, or disruptions in metabolic pathways, all of which can influence the overall prognosis of cancer patients^[69]11–[70]13. Additionally, the unpredictability of DILI, coupled with the lack of specific biomarkers and diagnostic criteria, complicates timely identification and management, highlighting the urgent need for enhanced monitoring strategies^[71]14. Our study aims to systematically assess the risk of liver injury induced by taxanes through comprehensive analysis of large pharmacovigilance databases and to reveal potential biological mechanisms by identifying key genes and signaling pathways. These findings will provide valuable insights for drug safety monitoring and offer important references for the safe