Abstract
Background
Nutrition and inflammation play vital roles in the prognosis of cancer patients. This study evaluated the impact of relevant indicators-albumin-globulin score (AGS), psoas muscle index (PMI), and the combination of AGS and PMI (CAP)- on postoperative outcomes in combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CC) patients.
Methods
One hundred forty one cHCC-CC patients treated surgically from 2010 to 2020 were retrospectively analyzed. Psoas muscle area was measured via abdominal CT scans. Albumin (ALB), globulin (GLB), and PMI cutoff values were established through receiver operating characteristic (ROC) curves. The influence of AGS, PMI, and CAP on cHCC-CC prognosis was evaluated using Time-dependent ROC curves. The predictive accuracy of nomograms were evaluated using calibration plots, the C-index value, and the area under the ROC curves (AUC).
Results
Lower PMI and higher AGS indicated poorer OS and DFS. A lower CAP grade was linked to a more favorable prognosis, with CAP grade 1 showing the best and grade 3 the worst prognosis. The Td-ROC curve demonstrates that CAP grade outperforms PMI and AGS in terms of predictive ability at various time points. The predictive accuracy of nomograms was considered acceptable, with c-index values of 0.684 (95% CI: 0.632–0.731) and 0.684 (95% CI: 0.635–0.734) for OS and DFS, respectively.The corresponding AUC values for 1, 3, and 5-year OS were 0.731, 0.744, and 0.720, and for DFS were 0.768, 0.796, and 0.757.
Conclusions
The CAP grade, integrating AGS and PMI, significantly correlates with postoperative prognosis in cHCC-CC patients. It stands as a crucial indicator for optimizing treatment planning.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12957-025-04000-z.
Keywords: Albumin–Globulin score, Psoas muscle index, Combined hepatocellular-cholangiocarcinoma, Survival, Prognosis
Introduction
Liver cancer ranks fourth among cancer-related deaths globally, with approximately 841,000 new cases and 782,000 deaths annually [1]. Combined hepatocellular cholangiocarcinoma (cHCC-CC) is a rare form of primary liver cancer, representing only 1%−5% of cases, characterized by a combination of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC) features [2, 3], it has been reported that the survival outcome of cHCC-CC is worse than that of HCC and CC [4–6].
Surgical resection is generally considered to be the best treatment for cHCC-CC, but even after surgical resection, the recurrence rate of the tumor is high (up to 80% in 5 years), and the 5-year survival rate remains below 30% [7, 8]. Due to its low prevalence and cHCC-CC takes into account the pathological and molecular characteristics of HCC and CC, the prognostic evaluation system for cHCC-CC is still immature [6, 9, 10].
Chronic inflammation plays an important role in the occurrence and development of tumors by affecting immune function and changing tumor microenvironment [11–13]. Albumin (ALB) and globulin (GLB) are two major protein components of plasma, and they exhibit distinct roles in the inflammatory response. ALB exerts anti-inflammatory effects through various mechanisms, including the inhibition of cytokine activation, dilution and neutralization of inflammatory mediators, antioxidation, modulation of immune response, and reduction of vascular permeability [14, 15]. On the other hand, GLB indirectly influences the inflammatory response by activating immune cells through certain components, such as complement proteins, that regulate inflammation [16].
The albumin-to-globulin ratio (AGR) and the model albumin-globulin score (AGS) based on ALB and GLB have been identified as independent prognostic factors in various cancers, including bladder cancer [17], liver cancer [18], intrahepatic cholangiocarcinoma [19], and non-small cell lung cancer (NSCLC) [20]. These markers are readily available from routine blood tests and reflect both nutritional status and chronic inflammation. By integrating multiple physiological dimensions, they offer a more comprehensive prognostic evaluation than single inflammatory markers and may improve predictive accuracy [21, 22]. However, there have been no studies investigating the impact of AGS and AGR on the long-term prognosis of patients with cHCC-CC.
Sarcopenia is widely recognized as a complex syndrome characterized by progressive and generalized loss of skeletal muscle mass, strength, and function [23–25]. While initially regarded as an age-related condition, advancing research has revealed that skeletal muscle depletion is a common yet often underestimated complication in patients with cirrhosis and cancer [26, 27]. Moreover, previous studies have also focused on sarcopenia as a significant prognostic factor in individuals with liver cancer [28–30].
The combined assessment of nutritional status and systemic inflammation plays a critical role in predicting the postoperative prognosis of patients with cHCC-CC. This study aimed to evaluate the prognostic significance of the AGS and the PMI in patients with cHCC-CC. Furthermore, we investigated the utility of the CAP grade, a novel indicator integrating AGS and PMI, to comprehensively assess nutritional and inflammatory status and its impact on long-term postoperative outcomes in cHCC-CC patients.
Methods
Study population
This study included 141 patients diagnosed with cHCC-CC confirmed by pathology who underwent liver resection at the Department of Liver Surgery, West China Hospital, between 2010 and 2020. The following exclusion criteria were applied: (1) receipt of non-resection treatments (chemotherapy, targeted therapy, or immunotherapy prior to surgery; (2) palliative resection (R2 resection) or biopsy only; (3) absence of laboratory test data or CT scan images within one month prior to surgery; (4) presence of other malignancies; (5) emergency surgery; (6) dead within one month after surgery. All patients have provided signed informed consent for the surgical procedure and expressly consented to the utilization of their clinical data for scientific research. This study obtained ethical approval from the Ethics Committee of West China Hospital, adhering to the guidelines of the 1975 Declaration of Helsinki.
Data collection and follow-up
The clinical and pathological information of the patients was collected from the hospital electronic medical records, surgical records, and pathological reports. It includes general characteristics such as age at the time of diagnosis, gender, and body mass index (BMI). BMI was categorized into different groups based on the following ranges: underweight (BMI < 18.5 kg/m²), normal weight (18.5 ≤ BMI < 24.0 kg/m²), overweight (24.0 ≤ BMI < 28.0 kg/m²), and obese (BMI ≥ 28.0 kg/m²) [31]. Preoperative routine laboratory tests include alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), carbohydrate antigen 19 − 9 (CA19-9), HBV/HCV infection status, albumin (ALB), globulin (GLB), etc. Tumor-related information comprises tumor size, number of tumors, vascular invasion, lymph node (LN) metastasis, tumor differentiation grade, margin status, capsule involvement, and satellite nodules. Surgical-related information includes anatomical resection, resection range, and types of surgery.
After surgery, patients were primarily followed up in outpatient settings, supplemented with regular telephone follow-ups, to record the patients’ survival and tumor recurrence status. The follow-up plan was as follows: monthly examinations in the first postoperative month, followed by visits every 3 months for the first 2 years, every 6 months from the third to the fifth year, and annual visits thereafter. Follow-up assessments include hepatic ultrasound, contrast-enhanced CT or MRI scans, as well as laboratory tests for tumor markers (CEA, CA19-9, AFP) and liver function evaluation. Overall survival (OS) was defined as the time from surgery to death or the last follow-up. Disease-free survival (DFS) was calculated from the date of surgery to the date of disease recurrence or the last follow-up date.
Nutritional status and inflammation assessment
Preoperative biochemical tests routinely measure albumin (ALB) and globulin (GLB) levels. The albumin-to-globulin ratio (AGR) is defined as the ratio of ALB to GLB. Using ROC curve analysis, the optimal cutoff values for ALB, GLB, and AGR were determined. Based on previous studies [20], The AGS (Albumin-Globulin Score) was defined as follows: patients with hypoalbuminemia and hyperglobulinemia were assigned an AGS of 2, those with normal values were assigned an AGS of 0, and patients with abnormality in either ALB or GLB were assigned an AGS of 1. AGS (1/2) was defined as high AGS, while AGS (0) was defined as low AGS.
All patients underwent abdominal contrast-enhanced CT examination within one month prior to surgery. The psoas muscle index (PMI) was employed for sarcopenia identification, defined as the psoas muscle area (PMA) at the axial imaging level of the third lumbar vertebra (L3) divided by the square of height [32]. The measurements of skeletal muscles from anonymous abdominal CT scans performed within 1 month before surgery were obtained by two trained independent researchers and averaged. Based on gender-specific optimal cutoff values for PMI (5.15 for males and 4.12 for females), patients were classified into low PMI group and high PMI group. Subsequently, the CAP was classified into the following grades: patients with low AGS (0) and high PMI were classified as CAP grade 1, patients with high AGS (1/2) and low PMI were all classified as CAP grade 3, and other patients were classified as CAP grade 2.
Statistical analysis
Statistical analysis was performed using R software (version 4.1.0). Continuous variables were compared using either the student’s t-test or the Wilcoxon signed-rank test. Categorical variables were compared using either Pearson’s χ2 test or Fisher’s exact test. The cutoff value of ALB, GLB, AGR, and PMI was determined by identifying the point on the ROC curves that maximized the Youden index, with a 2-year survival time as the endpoint. The Td-ROC curve was employed to compare the area under the curve (AUC) of AGS, PMI, and CAP at different time points. Kaplan-Meier survival curves were analyzed using the log-rank test. The Cox proportional hazards regression model was used to identify the independent factors of long-term prognosis. The univariate analysis was utilized to screen potential prognostic factors for OS and DFS, and those with a P value of < 0.05 were further evaluated in the multivariate analysis. A nomogram was developed using the independent risk factors. Evaluation of the nomogram’s predictive ability included the use of Harrell’s concordance index (C-index), calibration plot, and the area under the receiver operating characteristic (ROC) curve (AUC).Bilateral tests were used for all statistical tests, and a P-value < 0.05 was considered statistically significant.
Results
169 patients were initially identified. Following further screening, 28 patients lacking ALB, GLB, or preoperative CT images were considered ineligible and subsequently excluded, leaving a final study cohort of 141 patients.
Basic clinicopathological characteristics of the study cohort
There were no statistically significant differences in baseline characteristics between the study cohort and the excluded 28 patients (Table S1). The baseline characteristics of the 141 patients in the study cohort were presented in Table 1. The mean age of the study population was 51.79 years, with a majority of male patients comprising 110 cases (78.01%). The prevalence of hepatitis B infection was significantly higher than that of hepatitis C infection (79.43% vs. 2.13%), mirroring the HCC disease background in China. The median BMI was 23.18 kg/m²(IQR:20.83, 24.97), and the median psoas muscle index (PMI) was 5.11 cm²/m² (IQR: 4.21, 6.49). Regarding the plasma protein levels, the median albumin (ALB) concentration was 39.5 g/L (IQR: 38.1, 42.2), and the median globulin (GLB) concentration was 29.1 g/L (IQR: 27.0, 33.1). At the last follow-up, 110 patients (78.01%) had died. The average OS was 26.91 months (95% CI: 22.53–31.30), and the average DFS was 14.06 months (95% CI: 10.62–17.51).
Table 1.
The baseline characteristics of included cHCC-CC patients
| Variables | N (%) |
|---|---|
| Gender | |
| Male | 110(78.01) |
| Female | 31 (21.99) |
| Age(years, mean (SD)) | 51.79 (10.54) |
| Age (years) | |
| ≤ 60 | 110 (78.01) |
| > 60 | 31 (21.99) |
| BMI (kg/m2,median (IQR)) | 23.18 (20.83, 24.97) |
| BMI category | |
| Underweight | 8 (5.67) |
| Normal | 80 (56.74) |
| Overweight | 47 (33.33) |
| Obese | 6 (4.26) |
| Performance status | |
| 0 | 110 (78.01) |
| 1 | 18 (12.77) |
| 2 | 13 (9.22) |
| Tumor differentiation | |
| Well/moderate | 93 (65.96) |
| Poorly | 48 (34.04) |
| HBV | |
| No | 29 (20.57) |
| Yes | 112 (79.43) |
| HCV | |
| No | 138 (97.87) |
| Yes | 3 (2.13) |
| Cirrhosis | |
| No | 55(39.01) |
| Yes | 86(60.99) |
| Alcoholic hepatitis | |
| No | 137 (97.16) |
| Yes | 4 (2.84) |
| Diabetes mellitus | |
| No | 118(83.69) |
| Yes | 23(16.31) |
| Child-Pugh score | |
| 5 | 95(67.38) |
| 6 | 35(24.82) |
| 7–9 | 11(7.80) |
| Ascites | |
| No | 123 (87.23) |
| Yes | 18 (12.77) |
| ALT (IU/L, median (IQR)) | 36 (26–50) |
| AST (IU/L, median (IQR)) | 36 (29–48) |
| Total bilirubin (µmol/L, median (IQR)) | 13.2 (9.7–18) |
| Platelet count (×109/L, mean (SD)) | 146.06 (70.51) |
| Creatinine (µmol/L, mean (SD)) | 71.59 (15.63) |
| ALB (g/L, median (IQR)) | 39.5 (38.1, 42.2) |
| GLB (g/L, median (IQR)) | 29.1 (27.0, 33.1) |
| AGR (mean (SD)) | 1.36 (0.31) |
| AGS | |
| 0 | 27 (19.14) |
| 1–2 | 114 (80.86) |
| PMA (median (IQR)) | 14.26 (11.57, 19.28) |
| PMI (cm2/m2,median (IQR)) | 5.11(4.21, 6.49) |
| INR (mean (SD)) | 1.09 (0.15) |
| AFP (ng/ml) | |
| ≥ 400 | 41 (29.08) |
| < 400 | 100 (70.92) |
| CA19-9 (U/ml) | |
| ≤37 | 76 (53.90) |
| >37 | 65 (46.10) |
| CEA(ng/ml) | |
| ≤5 | 112 (79.43) |
| >5 | 29 (20.57) |
| Resection range | |
| Minor | 57 (40.43) |
| Major | 84 (59.57) |
| Types of surgery | |
| Laparoscope | 8 (5.67%) |
| Open operation | 133 (94.33) |
| Anatomic resection | |
| No | 70 (49.65) |
| Yes | 71 (50.35) |
| Tumor size(cm, mean (SD)) | 6.72 (3.17) |
| Tumor size | |
| ≤ 5 cm | 57 (40.43) |
| > 5 cm | 84 (59.57) |
| Tumor number | |
| Single | 66 (46.81) |
| Multiple | 75 (53.19) |
| Satellite nodule | |
| No | 91 (64.54) |
| Yes | 50 (35.46) |
| Capsule involvement | |
| No | 47 (33.33) |
| Yes | 94 (66.67) |
| LN positive | |
| No | 120 (85.11) |
| Yes | 21 (14.89) |
| Margin status | |
| R0 | 130 (92.20) |
| R1 | 11 (7.80) |
| Vascular invasion | |
| No | 76 (53.90) |
| Yes | 65 (46.10) |
| NASH | |
| No | 136 (96.45) |
| Yes | 5 (3.55) |
| HCC-TNM stage | |
| I | 19 (13.48) |
| II | 17 (12.05) |
| III | 80 (56.74) |
| IV | 25 (17.73) |
| ICC-TNM stage | |
| I | 22 (15.60) |
| II | 26 (18.44) |
| III | 93 (65.96) |
| Complications (Clavien-Dindo classification) | |
| 1 | 128 (90.78) |
| 2 | 11 (7.80) |
| ≥ 3 | 2 (1.42) |
| Postoperative hospital stay (days, median (IQR)) | 7(6–8) |
| DFS (months, mean (95%CI)) | 14.06 (10.62, 17.51) |
| Overall survival(months, mean (95%CI)) | 26.91 (22.53, 31.30) |
HBV hepatitis B virus; HCV Hepatitis C virus, PMA psoas muscle area, PMI psoas muscle index, ALB Albumin, GLB Globulin, AGR Albumin-to-globulin ratio, AGS Albumin-globulin score, INR International Normalized Ratio, LN Lymph node, NASH Nonalcoholic steatohepatitis, AFP Alpha-fetoprotein, CEA Carcinoembryonic antigen, CA19-9 Carbohydrate antigen 19 − 9, BMI Body mass index, IQR Interquartile Range, HCC-TNM Tumor-node-metastasis stage of hepatocellular carcinoma, ICC-TNM tumor-node-metastasis stage of intrahepatic cholangiocarcinoma, OS Overall survival, DFS Disease-free survival
Analysis of clinical features based on AGS, PMI, and CAP
According to the patients’ survival status, preoperative levels of albumin (ALB), globulin (GLB), albumin-to-globulin ratio (AGR), and psoas muscle index (PMI) were classified and represented in violin plots (Fig. 1). The results revealed a substantial association between elevated ALB levels and enhanced survival outcomes (p < 0.01, Fig. 1A), whereas GLB levels did not show a significant correlation with survival (p > 0.05, Fig. 1B). The cohort with increased AGR demonstrated superior survival outcomes (p < 0.01, Fig. 1C), and higher PMI levels were linked to an improved prognosis (p < 0.01, Fig. 1D). The optimal cutoff values were determined as follows: 40.15 g/L for ALB, 27.75 g/L for GLB, and 1.48 for AGR. The survival analysis demonstrated that patients with high AGR (> 1.48) exhibited significantly better OS (p < 0.01) (Fig. 2A) and DFS (p < 0.01) (Fig. 2D) compared to those with low AGR (≤ 1.48).
Fig. 1.
Association of preoperative biomarkers and psoas muscle index with survival outcomes. A Violin plot showing albumin (ALB) levels stratified by survival status. Higher ALB levels were significantly associated with improved survival (p < 0.01). B Violin plot of globulin (GLB) levels classified by survival status. No significant correlation was observed between GLB levels and survival (p > 0.05). C Violin plot of albumin-to-globulin ratio (AGR). Elevated AGR demonstrated superior survival outcomes (p < 0.01). D Violin plot of psoas muscle index (PMI). Higher PMI levels were linked to improved prognosis (p < 0.01)
Fig. 2.
Prognostic impact of albumin-globulin score (AGS), albumin-to-globulin ratio (AGR) and psoas muscle index (PMI) on survival outcomes. A Kaplan-Meier survival curve showed that the high AGR group had longer OS than the low AGR group (p < 0.01). B Kaplan-Meier survival curve showed that the low AGS group had longer OS than the high AGS group (p < 0.01). C Kaplan-Meier survival curve showed that the high PMI group had longer OS than the low PMI group (p = 0.02). D Kaplan-Meier survival curve showed that the high AGR group had longer DFS than the low AGR group (p < 0.01). E Kaplan-Meier survival curve showed that the low AGS group had longer DFS than the high AGS group (p < 0.01). F Kaplan-Meier survival curve showed that the high PMI group had longer OS than the low PMI group (p < 0.01)
The AGS assignment for different patients was as follows: (1) AGS of 2: Patients with ALB ≤ 40.15 g/L and GLB > 27.75 g/L; (2) AGS of 0: Patients with normal values (ALB > 40.15 g/L, GLB ≤ 27.75 g/L), (3) AGS of 1: Patients with abnormalities in either ALB or GLB, but not both. AGS (1/2) was defined as high AGS, while AGS (0) was defined as low AGS. According to the AGS stratification, patients were characterized and compared as outlined in Table 2. Among the cohort,27 patients were categorized as low AGS, while 114 patients were classified as high AGS. Compared to the high AGS group, the low AGS group exhibited a higher proportion of individuals with normal CA19-9 levels, a greater percentage of patients with lower ICC TNM staging, and better OS and DFS outcomes. No significant differences were observed in other variables. Kaplan-Meier survival curves displayed similar results, confirming a significant association between low AGS and improved OS (p < 0.01, Fig. 2B) as well as DFS (p < 0.01, Fig. 2E).
Table 2.
Baseline characteristics of the subgroups after stratifying based on AGS and PMI
| Variables | AGS | P value | PMI | P value | ||
|---|---|---|---|---|---|---|
| High(1/2) | Low(0) | High | Low | |||
| n | 114 | 27 | 84 | 57 | ||
| Gender | 0.771 | 0.688 | ||||
| Male | 90 (78.95) | 20 (74.07) | 67 (79.76) | 43 (75.44) | ||
| Female | 24 (21.05) | 7 (25.93) | 17 (20.24) | 14 (24.56) | ||
| Age(years, mean (SD)) | 51.72 (10.86) | 52.11(9.23) | 0.863 | 50.08 (10.09) | 54.32(10.75) | 0.019 |
| Age (years) | 0.822 | 0.004 | ||||
| ≤ 60 | 88 (77.19) | 22 (81.48) | 73 (86.90) | 37 (64.91) | ||
| > 60 | 26 (22.81) | 5 (18.52) | 11 (13.10) | 20 (35.09) | ||
| BMI (kg/m2,median (IQR)) | 23.02(20.92,24.84) | 23.44(20.80,25.40) | 0.561 | 23.63(21.66, 25.39) | 22.04(20.28, 24.61) | 0.032 |
| BMI category | 0.212 | 0.098 | ||||
| Underweight | 7 (6.14) | 1 (3.70) | 4 (4.76) | 4 (7.02) | ||
| Normal | 64 (56.14) | 16 (59.26) | 43 (51.19) | 37 (64.91) | ||
| Overweight | 40 (35.09) | 7 (25.93) | 31 (36.90) | 16 (28.07) | ||
| Obese | 3 (2.63) | 3 (11.11) | 6 (7.14) | 0 (0.00) | ||
| Performance status | 0.628 | 0.005 | ||||
| 0 | 88 (77.19) | 22 (81.48) | 73 (86.90) | 37 (64.91) | ||
| 1 | 16 (14.04) | 2 (7.41) | 5 (5.95) | 13 (22.81) | ||
| 2 | 10 (8.77) | 3 (11.11) | 6 (7.14) | 7 (12.28) | ||
| Tumor differentiation | 0.889 | 1.000 | ||||
| Well/moderate | 76 (66.67) | 17 (62.96) | 55 (65.48) | 38 (66.67) | ||
| Poorly | 38 (33.33) | 10 (37.04) | 29 (34.52) | 19 (33.33) | ||
| HBV | 0.277 | 0.109 | ||||
| No | 26 (22.81) | 3 (11.11) | 13 (15.48) | 16 (28.07) | ||
| Yes | 88 (77.19) | 24 (88.89) | 71 (84.52) | 41 (71.93) | 0.733 | |
| HCV | 0.170 | |||||
| No | 113 (99.12) | 25 (92.59) | 83 (98.81) | 55 (96.49) | ||
| Yes | 1 (0.88) | 2 (7.41) | 1 (1.19) | 2 (3.51) | ||
| Cirrhosis | 0.651 | 0.096 | ||||
| No | 46 (40.35) | 9 (33.33) | 38 (45.24) | 17 (29.82) | ||
| Yes | 68 (59.65) | 18 (66.67) | 46 (54.76) | 40 (70.18) | ||
| Alcoholic hepatitis | 1.000 | 0.361 | ||||
| No | 111 (97.37) | 26 (96.30) | 83 (98.81) | 54 (94.74) | ||
| Yes | 3 (2.63) | 1 (3.70) | 1 (1.19) | 3 (5.26) | ||
| Diabetes mellitus | 0.600 | 0.194 | ||||
| No | 94 (82.46) | 24 (88.89) | 67 (79.76) | 51 (89.47) | ||
| Yes | 20 (17.54) | 3 (11.11) | 17 (20.24) | 6 (10.53) | ||
| Child-Pugh score | 0.823 | 0.300 | ||||
| 5 | 77 (67.54) | 18 (66.67) | 57 (67.86) | 38 (66.67) | ||
| 6 | 27 (23.68) | 8 (29.63) | 21 (25.00) | 14 (24.56) | ||
| 7–9 | 10(8.77) | 1(3.70) | 6 (7.14) | 5 (8.77) | ||
| Child-Pugh stage | 0.628 | 0.973 | ||||
| A | 104 (91.23) | 26 (96.30) | 78 (92.86) | 52 (91.23) | ||
| B | 10 (8.77) | 1 (3.70) | 6 (7.14) | 5 (8.77) | ||
| Ascites | 1.000 | 0.690 | ||||
| No | 99 (86.84) | 24 (88.89) | 72 (85.71) | 51 (89.47) | ||
| Yes | 15 (13.16) | 3 (11.11) | 12 (14.29) | 6 (10.53) | ||
| ALT (IU/L, median (IQR)) | 34.50(26.00,48.00) | 38.00(27.00,53.00) | 0.543 | 40(28.75, 57) | 32(22, 44) | 0.007 |
| AST (IU/L, median (IQR)) | 35.00(28.25,47.50) | 40.00(32.50,53.00) | 0.297 | 36.5 (29.75, 48.25) | 34(28, 45) | 0.364 |
| Total bilirubin (µmol/L, median (IQR)) | 13.00(9.45,18.08) | 13.30(10.80,16.15) | 0.580 | 13.25(10.1, 18.13) | 12.8(9.7, 17) | 0.462 |
| Platelet count (×109/L, mean (SD)) | 150.28 (74.00) | 128.26 (50.67) | 0.1452 | 140.96 (64.08) | 153.58(79.05) | 0.299 |
| Creatinine (µmol/L, mean (SD)) | 71.99 (15.91) | 69.91 (14.53) | 0.536 | 70.63 (15.53) | 73.01 (15.80) | 0.377 |
| ALB (g/L, median (IQR)) | 39.15(37.15,40.08) | 42.00(41.20, 44.75) | < 0.001 | 39.80(38.60, 42.60) | 39.30(36.50, 41.80) | 0.025 |
| GLB (g/L, median (IQR)) | 29.90(27.70,34.90) | 26.60(24.95,27.00) | < 0.001 | 28.90(26.98, 33.03) | 29.10(27.10, 33.20) | 0.610 |
| PT(median (IQR)) | 12.05(11.40,12.90) | 11.80(11.40,13.30) | 0.733 | 12.0(11.3, 12.8) | 11.9 (11.4, 13.3) | 0.694 |
| AFP (ng/ml) | 0.760 | 1.000 | ||||
| ≥ 400 | 32 (28.07) | 9 (33.33) | 24 (28.57) | 17 (29.82) | ||
| < 400 | 82 (71.93) | 18 (66.67) | 60 (71.43) | 40 (70.18) | ||
| CA19-9 (U/ml) | 0.034 | 0.032 | ||||
| ≤37 | 56 (49.12) | 20 (74.07) | 52 (61.90) | 24 (42.11) | ||
| >37 | 58 (50.88) | 7 (25.93) | 32 (38.10) | 33 (57.89) | ||
| CEA(ng/ml) | 0.577 | 1.000 | ||||
| ≤5 | 89 (78.07) | 23 (85.19) | 67 (79.76) | 45 (78.95) | ||
| >5 | 25 (21.93) | 4 (14.81) | 17 (20.24) | 12 (21.05) | ||
| Resection range | 0.537 | 0.374 | ||||
| Minor | 48 (42.11) | 9 (33.33) | 37 (44.05) | 20 (35.09) | ||
| Major | 66 (57.89) | 18 (66.67) | 47 (55.95) | 37 (64.91) | ||
| Types of surgery | 0.976 | 1.000 | ||||
| Laparoscope | 7 (6.14) | 1 (3.70) | 5 (5.95) | 3 (5.26) | ||
| Open operation | 107 (93.86) | 26 (96.30) | 79 (94.05) | 54 (94.74) | ||
| Anatomic resection | 0.967 | 0.337 | ||||
| No | 56 (49.12) | 14 (51.85) | 45 (53.57) | 25 (43.86) | ||
| Yes | 58 (50.88) | 13 (48.15) | 39 (46.43) | 32 (56.14) | ||
| Tumorsize(cm, median (IQR)) | 6.20(4.00,8.95) | 6.00(4.40,8.25) | 0.803 | 6.0(4.0, 8.0) | 6.5(5.0, 9.3) | 0.153 |
| Tumor size | 1.000 | 0.215 | ||||
| ≤ 5 cm | 46 (40.35) | 11 (40.74) | 38 (45.24) | 19 (33.33) | ||
| > 5 cm | 68 (59.65) | 16 (59.26) | 46 (54.76) | 38 (66.67) | ||
| Tumor number | 0.712 | 0.014 | ||||
| Single | 52 (45.61) | 14 (51.85) | 47 (55.95) | 19 (33.33) | ||
| Multiple | 62 (54.39) | 13 (48.15) | 37 (44.05) | 38 (66.67) | ||
| Satellite nodule | 0.631 | 0.238 | ||||
| No | 72 (63.16) | 19 (70.37) | 58 (69.05) | 33 (57.89) | ||
| Yes | 42 (36.84) | 8 (29.63) | 26 (30.95) | 24 (42.11) | ||
| Capsule involvement | 1.000 | 0.856 | ||||
| No | 38 (33.33) | 9 (33.33) | 29 (34.52) | 18 (31.58) | ||
| Yes | 76 (66.67) | 18 (66.67) | 55 (65.48) | 39 (68.42) | ||
| LN positive | 0.754 | 0.626 | ||||
| No | 96 (84.21) | 24 (88.89) | 73 (86.90) | 47 (82.46) | ||
| Yes | 18 (15.79) | 3 (11.11) | 11 (13.10) | 10 (17.54) | ||
| Margin status | 0.200 | 0.189 | ||||
| R0 | 103 (90.35) | 27 (100.00) | 80 (95.24) | 50 (87.72) | ||
| R1 | 11 (9.65) | 0 (0.00) | 4 (4.76) | 7 (12.28) | ||
| Vascular invasion | 0.982 | 51 (60.71) | 0.072 | |||
| No | 62 (54.39) | 14 (51.85) | 25 (43.86) | |||
| Yes | 52 (45.61) | 13 (48.15) | 33 (39.29) | 32 (56.14) | ||
| NASH | 1.000 | 0.629 | ||||
| No | 110 (96.49) | 26 (96.30) | 80 (95.24) | 56 (98.25) | ||
| Yes | 4 (3.51) | 1 (3.70) | 4 (4.76) | 1 (1.75) | ||
| HCC-TNM stage | 0.497 | 0.097 | ||||
| I | 14 (12.28) | 5 (18.52) | 16 (19.05) | 3 (5.26) | ||
| II | 12 (10.53) | 5 (18.52) | 10 (11.90) | 7 (12.28) | ||
| III | 67 (58.77) | 13 (48.15) | 46 (54.76) | 34 (59.65) | ||
| IV | 21 (18.42) | 4 (14.81) | 12 (14.29) | 13 (22.81) | ||
| ICC-TNM stage | 0.002 | 0.026 | ||||
| I | 17 (14.91) | 5 (18.52) | 17 (20.24) | 5 (8.77) | ||
| II | 15 (13.16) | 11 (40.74) | 19 (22.62) | 7 (12.28) | ||
| III | 82 (71.93) | 11 (40.74) | 48 (57.14) | 45 (78.95) | ||
| Postoperative hospital stay (days, median (IQR)) | 7.00 (6.00, 8.00) | 8.00(6.00, 9.00) | 0.406 | 7.00 (6.00, 8.00) | 7.00 (6.00, 8.00) | 0.650 |
| DFS (months, mean (95%CI)) | 11.99(8.49,15.49) | 22.80(12.65,32.94) | 0.021 | 17.64(12.53, 22.75) | 8.79(5.04, 12.55) | 0.012 |
| OS (months, mean (95%CI)) | 24.64(19.72,29.55) | 36.53(27.09,45.97) | 0.034 | 30.95 (24.87, 37.03) | 20.97(14.98, 26.96) | 0.027 |
HBV Hepatitis B virus, HCV Hepatitis C virus, AGS Albumin-globulin score, PMI psoas muscle index, ALB Albumin, GLB Globulin, LN Lymph node, NASH Nonalcoholic steatohepatitis, AFP Alpha-fetoprotein, CEA Carcinoembryonic antigen, CA19-9 Carbohydrate antigen 19 − 9, BMI Body mass index, IQR Interquartile Range, HCC-TNM Tumor-node-metastasis stage of hepatocellular carcinoma, ICC-TNM Tumor-node-metastasis stage of intrahepatic cholangiocarcinoma, OS Overall survival, DFS Disease-free survival
PMI exhibits gender-specificity, with the optimal cutoff value for males being 5.15 cm2/m2 and for females being 4.12 cm2/m2. Stratification based on PMI (Table 2). classified 84 patients (59.57%) as high PMI and 57 patients (40.43%) as low PMI. Compared to the low PMI group, the high PMI group had a lower mean age (50.08 years vs. 54.32 years, p = 0.019) and a higher proportion of patients aged ≤ 60 years 86.9% vs. 64.91%, p = 0.004). The high PMI group also had a higher BMI ((23.63,IQR: 21.66, 25.39) vs. (22.04, IQR: 20.28, 24.61), p = 0.032).The high PMI group exhibited better performance status (p = 0.005), higher levels of ALB (p = 0.025), a higher proportion of patients with CA19-9 ≤ 37 U/ml (p = 0.032), and a greater prevalence of solitary tumors compared to the low PMI group (55.95% vs. 33.33%, p = 0.014). There were no statistically significant differences in other variables between the two groups. The high PMI group demonstrated a pronounced advantage in terms of survival outcomes, with superior OS ((30.95, 95%CI:24.87–37.03) vs. (20.97, 95%CI: 14.98–26.96), p = 0.027) and a prolonged DFS ((17.64, 95%CI:12.53–22.75) vs. (8.79, 95%CI:5.04–12.55), p = 0.012). Consistent with these findings, the Kaplan-Meier survival curves clearly illustrated a significant association between high PMI and improved OS (p = 0.02, Fig. 2C) as well as DFS (p < 0.01, Fig. 2F).
According to the definition of CAP grading, 21 patients (14.89%) were classified as CAP grade 1, 69 patients (48.94%) as CAP grade 2, and 51 patients (36.17%) as grade 3 (Table 3). In the CAP grade 1 group, the proportion of patients age ≤ 60 years and patients with a performance status of 0 was the highest (85.71%), while it was the lowest in the CAP grade 3 group(64.71%). In the CAP grade 1 group, there was a significant increase in ALB levels (p < 0.001), but a decrease in GLB levels (p < 0.001), while in the CAP grade 3 group, the opposite pattern was observed. The statistical results indicate that patients in CAP grade 1 had longer OS and DFS compared to patients in CAP grade 2/3 (both p < 0.05). According to the Kaplan-Meier survival curves, patients in the CAP grade 1 group had the best OS and DFS compared to grade 2/3 (both p < 0.01, Fig. 3A, B). Subsequently, td-ROC curve analysis was performed to evaluate the predictive abilities of CAP, AGS, and PMI for survival rates and disease-free survival rates, that at different time points, the AUC for CAP was consistently higher than that for AGS and PMI (Fig. 4A, B).
Table 3.
Baseline characteristics after stratifying the study cohort based on CAP grade
| Variables | CAP | P value | ||
|---|---|---|---|---|
| Grade 1 | Grade 2 | Grade 3 | ||
| n | 21 | 69 | 51 | |
| Gender | 0.094 | |||
| Male | 14 (66.67) | 59 (85.51) | 37 (72.55) | |
| Female | 7 (33.33) | 10 (14.49) | 14 (27.45) | |
| Age(years, mean (SD)) | 51.33 (9.16) | 50.12 (10.40) | 54.26 (10.95) | 0.101 |
| Age (years) | 0.016 | |||
| ≤ 60 | 18 (85.71) | 59 (85.51) | 33 (64.71) | |
| > 60 | 3 (14.29) | 10 (14.49) | 18 (35.29) | |
| BMI (kg/m2,mean (SD)) | 23.52(21.97,25.40) | 23.39(20.86,25.16) | 22.10(20.52,24.61) | 0.162 |
| BMI category | 0.088 | |||
| Underweight | 1 (4.76) | 3 (4.35) | 4 (7.84) | |
| Normal | 12 (57.14) | 35 (50.72) | 33 (64.71) | |
| Overweight | 5 (23.81) | 28 (40.58) | 14 (27.45) | |
| Obese | 3 (14.29) | 3 (4.35) | 0 (0.00) | |
| Performance status | 0.044 | |||
| 0 | 18 (85.71) | 59 (85.51) | 33 (64.71) | |
| 1 | 1 (4.76) | 5 (7.25) | 12 (23.53) | |
| 2 | 2 (9.52) | 5 (7.25) | 6 (11.76) | |
| Tumor differentiation | 0.914 | |||
| Well/moderate | 13 (61.90) | 46 (66.67) | 34 (66.67) | |
| Poorly | 8 (38.10) | 23 (33.33) | 17 (33.33) | |
| HBV | 0.096 | |||
| No | 1 (4.76) | 14 (20.29) | 14 (27.45) | |
| Yes | 20 (95.24) | 55 (79.71) | 37 (72.55) | |
| HCV | 0.651 | |||
| No | 20 (95.24) | 68 (98.55) | 50 (98.04) | |
| Yes | 1 (4.76) | 1 (1.45) | 1 (1.96) | |
| Cirrhosis | 0.581 | |||
| No | 9 (42.86) | 29 (42.03) | 17 (33.33) | |
| Yes | 12 (57.14) | 40 (57.97) | 34 (66.67) | |
| Alcoholic hepatitis | 0.660 | |||
| No | 21 (100.00) | 67 (97.10) | 49 (96.08) | |
| Yes | 0 (0.00) | 2 (2.90) | 2 (3.92) | |
| Diabetes mellitus | 0.096 | |||
| No | 19 (90.48) | 53 (76.81) | 46 (90.20) | |
| Yes | 2 (9.52) | 16 (23.19) | 5 (9.80) | |
| Child-Pugh score | 0.201 | |||
| 5 | 15 (71.43) | 45 (65.22) | 35 (68.63) | |
| 6 | 6 (28.57) | 17 (24.64) | 12 (23.53) | |
| 7–9 | 0 (0.00) | 7 (10.14) | 4 (7.84) | |
| Child-Pugh stage | 0.316 | |||
| A | 21 (100.00) | 62 (89.86) | 47 (92.16) | |
| B | 0 (0.00) | 7 (10.14) | 4 (7.84) | |
| Ascites | 0.954 | |||
| No | 18 (85.71) | 60 (86.96) | 45 (88.24) | |
| Yes | 3 (14.29) | 9 (13.04) | 6 (11.76) | |
| ALT (IU/L, median (IQR)) | 36.00(27.00,47.00) | 41.00(30.00,57.00) | 31.00(22.00,39.50) | 0.003 |
| AST (IU/L, median (IQR)) | 36.00(32.00,48.00) | 40.00(30.00,53.00) | 33.00(27.50,43.50) | 0.376 |
| Total bilirubin (µmol/L, median (IQR)) | 13.30(10.90,16.10) | 13.30(9.40,19.60) | 12.50(9.65,17.00) | 0.598 |
| Platelet count (×109/L, mean (SD)) | 122.76 (52.94) | 147.07 (64.54) | 154.29 (82.69) | 0.224 |
| Creatinine (µmol/L, mean (SD)) | 67.56 (14.14) | 72.22 (15.79) | 72.41(16.02) | 0.441 |
| ALB (g/L, median (IQR)) | 42.20(41.20,44.90) | 39.30(38.10,42.00) | 39.00(35.75,39.90) | < 0.001 |
| GLB (g/L, median (IQR)) | 26.70(25.20,27.00) | 29.60(27.30,34.90) | 29.50(27.40,33.65) | < 0.001 |
| PT(median (IQR)) | 11.80(11.30,13.50) | 12.00(11.40,12.70) | 11.90(11.40,13.30) | 0.807 |
| AFP (ng/ml) | 0.875 | |||
| ≥ 400 | 7 (33.33) | 19 (27.54) | 15 (29.41) | |
| < 400 | 14 (66.67) | 50 (72.46) | 36 (70.59) | |
| CA19-9 (U/ml) | 0.011 | |||
| ≤37 | 16 (76.19) | 40 (57.97) | 20 (39.22) | |
| >37 | 5 (23.81) | 29 (42.03) | 31 (60.78) | |
| CEA(ng/ml) | 0.742 | |||
| ≤5 | 18 (85.71) | 54 (78.26) | 40 (78.43) | |
| >5 | 3 (14.29) | 15 (21.74) | 11 (21.57) | |
| Resection range | 0.104 | |||
| Minor | 6 (28.57) | 34 (49.28) | 17 (33.33) | |
| Major | 15 (71.43) | 35 (50.72) | 34 (66.67) | |
| Types of surgery | 0.255 | |||
| Laparoscope | 0 (0.00) | 6 (8.70) | 2 (3.92) | |
| Open operation | 21 (100.00) | 63 (91.30) | 49 (96.08) | |
| Anatomic resection | 0.246 | |||
| No | 10 (47.62) | 39 (56.52) | 21 (41.18) | |
| Yes | 11 (52.38) | 30 (43.48) | 30 (58.82) | |
| Tumorsize(cm, median (IQR)) | 6.50(4.80,8.50) | 6.00(4.00,8.00) | 6.80(5.00,9.35) | 0.156 |
| Tumor size | 0.187 | |||
| ≤ 5 cm | 8 (38.10) | 33 (47.83) | 16 (31.37) | |
| > 5 cm | 13 (61.90) | 36 (52.17) | 35 (68.63) | |
| Tumor number | 0.021 | |||
| Single | 11 (52.38) | 39 (56.52) | 16 (31.37) | |
| Multiple | 10 (47.62) | 30 (43.48) | 35 (68.63) | |
| Satellite nodule | 0.370 | |||
| No | 16 (76.19) | 45 (65.22) | 30 (58.82) | |
| Yes | 5 (23.81) | 24 (34.78) | 21 (41.18) | |
| Capsule involvement | 0.870 | |||
| No | 8 (38.10) | 22 (31.88) | 17 (33.33) | |
| Yes | 13 (61.90) | 47 (68.12) | 34 (66.67) | |
| LN positive | 0.673 | |||
| No | 19 (90.48) | 59 (85.51) | 42 (82.35) | |
| Yes | 2 (9.52) | 10 (14.49) | 9 (17.65) | |
| Margin status | 0.098 | |||
| R0 | 21 (100.00) | 65 (94.20) | 44 (86.27) | |
| R1 | 0 (0.00) | 4 (5.80) | 7 (13.73) | |
| Vascular invasion | 0.113 | |||
| No | 11 (52.38) | 43 (62.32) | 22 (43.14) | |
| Yes | 10 (47.62) | 26 (37.68) | 29 (56.86) | |
| NASH | 0.743 | |||
| No | 20 (95.24) | 66 (95.65) | 50 (98.04) | |
| Yes | 1 (4.76) | 3 (4.35) | 1 (1.96) | |
| HCC-TNM stage | 0.083 | |||
| I | 5 (23.81) | 11 (15.94) | 3 (5.88) | |
| II | 5 (23.81) | 5 (7.25) | 7 (13.73) | |
| III | 8 (38.10) | 43 (62.32) | 29 (56.86) | |
| IV | 3 (14.29) | 10 (14.49) | 12 (23.53) | |
| ICC-TNM stage | < 0.001 | |||
| I | 5 (23.81) | 12 (17.39) | 5 (9.80) | |
| II | 11 (52.38) | 8 (11.59) | 7 (13.73) | |
| III | 5 (23.81) | 49 (71.01) | 39 (76.47) | |
| Postoperative hospital stay (days, median (IQR)) | 8.00(7.00,9.00) | 7.00(6.00,8.00) | 7.00(6.50,8.00) | 0.360 |
| DFS (months, mean (95%CI)) | 25.75 (13.02,38.48) | 14.72(9.74,19.69) | 8.37 (4.29,12.44) | 0.004 |
| OS(months, mean (95%CI)) | 38.61 (27.18,50.03) | 28.47(21.78,35.17) | 19.99(13.54,26.44) | 0.018 |
HBV Hepatitis B virus, HCV Hepatitis C virus, ALB Albumin, GLB Globulin, LN Lymph node, NASH Nonalcoholic steatohepatitis, AFP Alpha-fetoprotein, CEA Carcinoembryonic antigen, CA19-9 Carbohydrate antigen 19 − 9, BMI Body mass index, IQR Interquartile Range, HCC-TNM Tumor-node-metastasis stage of hepatocellular carcinoma, ICC-TNM Tumor-node-metastasis stage of intrahepatic cholangiocarcinoma, OS Overall survival, DFS Disease-free survival
Fig. 3.
Impact of CAP grade on survival outcomes. A Kaplan-Meier survival curve showed that patients with CAP grade 1 exhibited significantly longer OS compared to those with CAP grade 2/3 (p < 0.01). B Kaplan-Meier survival curve showed that CAP grade 1 was associated with improved DFS outcomes relative to CAP grade 2/3 (p < 0.01)
Fig. 4.
Time-dependent ROC curve analysis for survival prediction demonstrated that CAP exhibited superior predictive accuracy (higher AUC) compared to AGS and PMI for both OS (A) and DFS (B)
Construction and evaluation of the nomogram
According to univariate and multivariate analyses, tumor number (HR: 1.62, 95% CI: 1.09–2.40, p = 0.018), margin status (HR: 2.52, 95% CI: 1.25–5.06, p = 0.010), vascular invasion (HR: 1.69, 95% CI: 1.14–2.49, p = 0.009), CAP grade 2 (HR: 2.24, 95% CI: 1.17–4.31, p = 0.015), and CAP grade 3 (HR: 2.50, 95% CI: 1.27–4.93, p = 0.008) were identified as independent prognostic factors for postoperative OS in cHCC-CC patients (Table 4). In terms of DFS, independent prognostic factors included HBV (HR: 1.77, 95% CI: 1.12–2.79, p = 0.015), ascites (HR: 2.29, 95% CI: 1.33–3.96, p = 0.003), resection range (HR: 1.51, 95% CI: 1.04–2.20, p = 0.031), margin status (HR: 2.94, 95% CI: 1.49–5.81, p = 0.002), vascular invasion (HR: 1.63, 95% CI: 1.10–2.42, p = 0.014), CAP grade 2 (HR: 1.82, 95% CI: 1.04–3.19, p = 0.037), and CAP grade 3 (HR: 2.43, 95% CI:1.34–4.41, p = 0.004) (Table 4). Then, we constructed nomograms based on respective risk factors to predict the 1-year, 3-year, and 5-year OS and DFS of cHCC-CC patients (Fig. 5A, B). The C-index values were 0.684 (95% CI: 0.632–0.731) and 0.684 (95% CI: 0.635–0.734), respectively. The calibration plots demonstrated good agreement between predicted and observed outcomes for OS and DFS at 1, 3, and 5 years (Fig. 6A, B). The ROC curves exhibited AUC values of 0.731, 0.744, and 0.720 for 1-year, 3-year, and 5-year OS, respectively (Fig. 6C), and the AUC values for the 1-year, 3-year, and 5-year DFS were 0.768, 0.796, and 0.757, respectively (Fig. 6D). Therefore, the constructed nomograms exhibited good predictive ability for postoperative OS and DFS in cHCC-CC patients.
Table 4.
Univariate and multivariate analysis of prognostic risk factors of OS and DFS in patients with cHCC-CC
| Variables | Univariate analysis (OS) | Multivariate analysis (OS) | Univariate analysis (DFS) | Multivariate analysis (DFS) | ||||
|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | |
| Gender (Male/Female) | 0.75(0.47,1.19) | 0.221 | 0.72(0.46,1,11) | 0.134 | ||||
| Age (≤ 60/>60years) | 0.90(0.57,1.43) | 0.667 | 1.12(0.74,1.71) | 0.584 | ||||
| BMI category | ||||||||
| Underweight | Reference | Reference | ||||||
| Normal | 1.79(0.65,4.92) | 0.259 | 1.40(1.61,3.22) | 0.431 | ||||
| Overweight | 1.38(0.49–3.88) | 0.546 | 1.23(0.52,2.91) | 0.632 | ||||
| Obese | 2.43(0.69,8.65) | 0.169 | 1.64(0.53,5.10) | 0.393 | ||||
| Performance status | ||||||||
| 0 | Reference | Reference | ||||||
| 1 | 1.05(0.60,1.82) | 0.867 | 0.99(0.58,1.68) | 0.966 | ||||
| 2 | 0.74(0.37,1.47) | 0.385 | 1.36(0.76,2.43) | 0.308 | ||||
| Tumor differentiation | ||||||||
| Well/moderate | Reference | Reference | ||||||
| Poorly | 0.99(0.67,1.48) | 0.984 | 1.07(0.74,1.54) | 0.726 | ||||
| HBV(No/Yes) | 0.97(0.61,1.54) | 0.887 | 1.57(1.02,2.40) | 0.040 | 1.77(1.12,2.79) | 0.015 | ||
| HCV(No/Yes) | 0.86(0.21,3.50) | 0.836 | 1.02(0.32,3.21) | 0.977 | ||||
| Cirrhosis (No/Yes) | 1.13(0.77,1.66) | 0.530 | 0.98(0.68,1.40) | 0.893 | ||||
| Alcoholic hepatitis (No/Yes) | 0.88(0.28,2.79) | 0.827 | 1.80(0.66,4.88) | 0.252 | ||||
| Diabetes mellitus (No/Yes) | 0.99(0.60,1.63) | 0.969 | 0.82(0.50,1.32) | 0.411 | ||||
| Child-Pugh score | ||||||||
| 5 | Reference | Reference | ||||||
| 6 | 1.20(0.77,1.87) | 0.421 | 0.88(0.58,1.35) | 0.562 | ||||
| 7–9 | 1.20(0.60,2.40) | 0.613 | 1.21(0.64,2.27) | 0.559 | ||||
| Child-Pugh stage(A/B) | 1.14(0.58,2.26) | 0.704 | 1.25(0.67,2.32) | 0.484 | ||||
| Ascites (No/Yes) | 1.50(0.87,2.59) | 0.147 | 1.88(1.12,3.15) | 0.017 | 2.29(1.33,3.96) | 0.003 | ||
| AFP (≥ 400/< 400ng/ml) | 1.05(0.69,1.58) | 0.832 | 1.22(0.83,1.79) | 0.305 | ||||
| CA19-9 (≤ 37/>37U/ml) | 0.92(0.63,1.34) | 0.652 | 1.32(0.92,1.88) | 0.126 | ||||
| CEA(> 5/≤5ng/ml) | 0.61(0.37,1.02) | 0.051 | 0.90(0.58,1.38) | 0.623 | ||||
| Resection range(Minor/Major) | 1.23(0.84,1.81) | 0.293 | 1.60(1.11,2.32) | 0.012 | 1.51(1.04,2.20) | 0.031 | ||
| Types of surgery (Laparoscope/Open operation) | 1.24(0.57,2.67) | 0.588 | 0.91(0.42,1.96) | 0.807 | ||||
| Anatomic resection (No/Yes) | 1.09(0.75,1.58) | 0.664 | 1.30(0.91,1.85) | 0.148 | ||||
| Tumor size (≤ 5 cm/>5 cm) | 1.30(0.89,1.92) | 0.174 | 1.42(0.99,2.04) | 0.059 | ||||
| Tumor number (Single/Multiple) | 1.72(1.18,2.52) | 0.005 | 1.62(1.09,2.40) | 0.018 | 1.36(0.97,1.94) | 0.087 | ||
| Satellite nodule (No/Yes) | 1.31(0.89,1.93) | 0.178 | 1.24(0.86,1.78) | 0.256 | ||||
| Capsule involvement (No/Yes) | 0.89(0.60,1.33) | 0.580 | 1.05(0.72,1.54) | 0.785 | ||||
| LN positive (No/Yes) | 1.35(0.81,2.25) | 0.245 | 1.10(0.67,1.80) | 0.704 | ||||
| Margin status (R0/R1) | 4.41(2.33,8.34) | < 0.001 | 2.52(1.25,5.06) | 0.010 | 4.05(2.12,7.76) | < 0.001 | 2.94(1.49,5.81) | 0.002 |
| Vascular invasion (No/Yes) | 1.87(1.28,2.73) | 0.001 | 1.69(1.14,2.49) | 0.009 | 1.55(1.09,2.20) | 0.016 | 1.63(1.10,2.42) | 0.014 |
| NASH (No/Yes) | 0.73(0.27,1.99) | 0.544 | 0.66(0.27,1.62) | 0.367 | ||||
| CAP | ||||||||
| Grade 1 | Reference | Reference | Reference | |||||
| Grade 2 | 2.07(1.08,3.97) | 0.028 | 2.24(1.17,4.31) | 0.015 | 1.78(1.02,3.09) | 0.041 | 1.82(1.04,3.19) | 0.037 |
| Grade 3 | 3.09(1.59,5.99) | < 0.001 | 2.50(1.27,4.93) | 0.008 | 2.67(1.50,4.73) | < 0.001 | 2.43(1.34,4.41) | 0.004 |
HBV Hepatitis B virus, HCV Hepatitis C virus, PMI psoas muscle index, LN Lymph node, NASH Nonalcoholic steatohepatitis, AFP Alpha-fetoprotein, CEA Carcinoembryonic antigen, CA19-9 Carbohydrate antigen 19 − 9, BMI Body mass index, CAP Combination of albumin-globulin score and psoas muscle index
Fig. 5.
Nomograms for predicting postoperative survival outcomes in cHCC-CC patients. A Nomogram for predicting 1-, 3-, and 5-year OS. The model incorporates independent prognostic factors identified by multivariate analysis, including tumor number, vascular invasion, margin status, and CAP grade. The concordance index (C-index) was 0.684 (95% CI: 0.632–0.731). B Nomogram for predicting 1-, 3-, and 5-year DFS. Prognostic variables include HBV status, ascites, resection range, margin status, vascular invasion, and CAP grade. The C-index was 0.684 (95% CI: 0.635–0.734)
Fig. 6.
Validation of nomogram predictive performance in cHCC-CC patients. A Calibration plots for 1-, 3-, and 5-year OS demonstrate strong agreement between predicted probabilities and observed outcomes, confirming reliable model calibration. B Calibration plots for 1-, 3-, and 5-year DFS demonstrate strong agreement between predicted probabilities and observed outcomes, confirming reliable model calibration. C ROC curves for OS prediction. The AUC values were 0.731 (1-year), 0.744 (3-year), and 0.720 (5-year), demonstrating robust discriminative ability. D ROC curves for DFS prediction. AUC values reached 0.768 (1-year), 0.796 (3-year), and 0.757 (5-year), confirming strong predictive performance
Discussion
The systemic nutritional and inflammatory status have a significant impact on the prognosis of malignant tumor patients. This retrospective study conducted a detailed analysis of the impact of the novel predictive factors, namely CAP grade, which integrates ALB, GLB, and PMI, on the long-term outcomes of postoperative cHCC-CC patients. CAP serves as an indicator that simultaneously reflects the nutritional and inflammatory status of patients. The results of the study demonstrated that CAP grade exhibited superior predictive ability for OS and DFS compared to individual AGS or PMI alone. Nomograms were constructed based on independent risk factors for OS and DFS, such as CAP, margin status, vascular invasion, etc. providing reference for clinical doctors to establish personalized postoperative treatment and follow-up strategies for different patients.
Inflammation elicits a cascade of intricate biological processes, including cellular injury, dysregulated proliferation, perturbations in the tumor microenvironment, and immune escape, fostering tumor initiation, progression, and metastasis [33, 34]. Studies suggests that systemic inflammation may exert a noteworthy influence on the prognostic outcomes of malignant tumors [11, 35, 36]. Serum ALB and GLB, frequently utilized markers of liver function, were known to be associated with systemic inflammation to a certain extent.
ALB plays a crucial role in maintaining normal nutritional status and immune function. Systemic malnutrition or wasting diseases such as malignant tumors and inflammatory bowel disease disrupt protein metabolism, leading to a disturbance in protein synthesis and an increase in protein breakdown. Consequently, serum albumin levels are reduced, particularly pronounced in patients with hepatitis and cirrhosis [37]. Notably, in China, chronic hepatitis B infection is the foremost cause of liver cancer, with 79.43% of the patients in this study being diagnosed with hepatitis B. Moreover, in a systemic inflammatory state, the activation of inflammatory responses triggers the release of cytokines, including tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), among others, which can inhibit the liver’s ability to synthesize albumin [38, 39]. Therefore, hypoproteinemia was often associated with systemic malnutrition and inflammatory conditions. Hypoalbuminemia has been shown to be associated with poor prognosis in hepatocellular carcinoma, intrahepatic cholangiocarcinoma (ICC), and bladder cancer [17–19]. the results of this study demonstrate that low ALB levels were indicative of a worse prognosis in patients with cHCC-CC.
Globulin is another important constituent of plasma proteins, including both immunoglobulins and non-immunoglobulin proteins. In the presence of systemic inflammation, immune cells are stimulated to secrete a greater quantity of immunoglobulins, leading to elevated serum globulin levels. Consequently, a high globulin level is commonly regarded as indicators of systemic inflammation and immune response. But the impact of globulin (GLB) levels on the prognosis of cancer patients remains inconclusive. Li et al. observed a significant correlation between elevated GLB levels and poorer prognosis in patients with ICC [19]. However, other studies have indicated that GLB levels do not have a significant impact on the prognosis of gastric cancer, pancreatic cancer, and bladder cancer [17, 40, 41], similarly, our study also found no definitive association between GLB levels and the prognosis of cHCC-CC patients.
ALB and GLB levels are influenced by liver function, renal excretion, fluid balance, nutrition, and other diseases. Relying solely on ALB and GLB measurements may not provide comprehensive information on systemic inflammation. It is necessary to consider other inflammatory markers and clinical manifestations for a more comprehensive evaluation. AGR, defined as the ratio of serum ALB to GLB (ALB/GLB). During systemic inflammation, the ALB/GLB ratio tends to decrease due to a decrease in albumin levels and an increase in globulin levels. AGR is considered a potential prognostic indicator in cancer prognosis assessment. Studies have shown that AGR is an independent prognostic factor in digestive system cancers, upper urinary tract urothelial carcinoma, oral squamous cell carcinoma, and hepatocellular carcinoma [11, 17, 18, 42]. The findings of this study also suggest that a higher AGR (> 1.48) is associated with a better prognosis. However, it is important to note that the predictive ability of AGR and its specific application range are still under investigation and may be influenced by various factors. subsequently, AGS (Albumin-Globulin Score) was proposed as another prognostic indicator. Previous studies have demonstrated the value of AGS in predicting the prognosis of liver cancer [18, 19] and lung cancer. In a study by Li et al. [20], they evaluated the prognostic value of preoperative AGR and AGS in 365 operable NSCLC patients, and found that AGS was an independent prognostic factor for OS and DFS, with better predictive ability than AGR. This study reports, for the first time, the impact of AGS on the prognosis of cHCC-CC patients. It demonstrates that a higher AGS level is associated with worse OS and DFS in cHCC-CC patients.
Sarcopenia refers to the gradual decline in skeletal muscle mass and function that occurs with age. Since assessing and quantifying muscle function is challenging, most studies opt for psoas muscle area (PMA) at the L3 level based on CT scans as a suitable method for evaluating muscle mass [32]. This measurement is preferred because it is easy to measure and is not affected by factors such as ascites or other confounding variables. We utilized psoas muscle index (PMI) to assess sarcopenia but HCC-CC patients lack standardized reference values for PMI. In our study, the cutoff values for male and female individuals were 5.15 and 4.12 cm2/m2, respectively, which align with previous research conducted in Asian populations [43–45]. Low PMI was identified as a risk factor for postoperative OS and DFS in cHCC-CC patients, consistent with previous research findings [45]. Interestingly, despite studies suggesting that decreased BMI levels are not necessarily associated with muscle loss [27], this study found that patients in the high PMI group had higher BMI compared to the low PMI group. The parallel changes in PMI and BMI observed in our study may be attributed to their shared association with nutritional status. In the context of chronic debilitating diseases like malignant tumors, it is plausible that these parameters exhibit a similar directionality.
In order to comprehensively assess the impact of nutritional and inflammatory status on the prognosis of cHCC-CC patients, we introduced a new composite index, the CAP grade, which combines AGS and PMI. The Td-ROC curve demonstrated that the predictive performance of the CAP grade was superior to that of AGS or PMI alone. Importantly, the analysis revealed that the CAP grade was an independent risk factor for postoperative OS and DFS in cHCC-CC patients. To our knowledge, this is the first study to integrate AGS and skeletal muscle status for predicting long-term prognosis in cHCC-CC patients undergoing surgical resection. The combined CAP grading system, which integrates the AGS and PMI, not only enhances prognostic accuracy but also offers meaningful clinical utility in guiding patient management. Preoperative identification of high-risk individuals, particularly those assigned to CAP grade 3, facilitates the implementation of targeted interventions, such as nutritional optimization, anti-inflammatory therapies, and structured prehabilitation programs aimed at improving functional status prior to surgery. These patients may also benefit from more rigorous postoperative monitoring and multidisciplinary evaluation to guide surgical decision-making and assess their potential eligibility for participation in clinical research. Accordingly, the CAP grading system provides a valuable basis for individualized prognostic assessment and therapeutic planning in patients at higher risk.
Nomogram is a commonly used predictive model for prognosis in malignant tumors. Therefore, we constructed nomograms based on CAP grade and other indicators to predict OS and DFS in patients with cHCC-CC after surgery. The excellent predictive ability of the two nomograms was evaluated through ROC curves and calibration plots, demonstrating their effectiveness in providing accurate prognostic predictions. These nomograms can serve as valuable tools for clinicians in optimizing postoperative follow-up and treatment strategies for cHCC-CC patients.
It is important to acknowledge the limitations of our study. Firstly, the low incidence rate of cHCC-CC resulted in a relatively small number of included patients, and the study was conducted at a single center, which may introduce bias due to unknown confounding factors. Secondly, we selected the results of the most recent preoperative examination before the date of surgery, which could be influenced by certain patients receiving preoperative treatments such as liver protection and albumin infusion, thereby affecting the true liver function status. Thirdly, we excluded some patients with incomplete clinical data, which may result in selection bias. In summary, our study findings require external validation in other centers or the implementation of more rigorous prospective international multicenter studies to further substantiate our discoveries.
Conclusions
This study demonstrates a strong correlation between CAP grade, a prognostic indicator combining preoperative AGS and PMI, and long-term prognosis in patients undergoing cHCC-CC surgery. Nomograms were constructed based on CAP grade, which will provide valuable guidance for personalized treatment approaches. Preoperative nutritional support and moderate exercise have a positive impact on improving the prognosis of cHCC-CC patients.
Supplementary Information
Acknowledgements
We thank the research team members for their hard work.
Authors’ contributions
Study design: ZQ, HW. Drafted the initial manuscript: CJ. Data collection: ZZ. Analysis of data: ZQ, ZZ. Analyses and reviewed the manuscript: HW. Conceptualization, project administration: HW. All authors approved the final manuscript.
Funding
This work was supported by grants from the National multidisciplinary collaborative diagnosis and treatment capacity building project for major diseases (TJZ202104), the Natural Science Foundation of China (82173248, 82272685, 82002967 and 82202260), the Postdoctoral Science the fellowship of China National Postdoctoral Program for Innative Talents (BX20200225), the Project funded by China Postdoctoral Science Foundation (2022TQ0221), the Science and Technology Major Program of Sichuan Province (2022ZDZX0019), the Sichuan Science and Technology Program (2023YFS0128, 2023NSFSC1874, 2021YJ0420), 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYJC18008, ZYGD22006), the Sichuan University postdoctoral interdisciplinary Innovation Fund (10822041A2103).
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
The study was approved by the ethics committee of West China Hospital of Sichuan University.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Zeyuan Qiang and Zheng Zhang contributed equally to this work.
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Data Availability Statement
No datasets were generated or analysed during the current study.